diff --git a/resume_matcher/apps/backend/app/cloud_session.py b/resume_matcher/apps/backend/app/cloud_session.py
new file mode 100644
index 0000000..be30bb7
--- /dev/null
+++ b/resume_matcher/apps/backend/app/cloud_session.py
@@ -0,0 +1,68 @@
+"""
+Peregrine cloud session — thin wrapper around cf_core.cloud_session.
+
+Sets a request-scoped ContextVar with the authenticated user_id so that
+_allocate_orch_async in llm.py can pass it to cf-orch without any service
+function signature changes.
+
+Usage — add to main.py once:
+
+    from app.cloud_session import session_middleware_dep
+    app = FastAPI(..., dependencies=[Depends(session_middleware_dep)])
+
+From that point, any route (and every service/llm function it calls)
+has access to the current user_id via llm.get_request_user_id().
+"""
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+from fastapi import Depends, Request, Response
+
+from circuitforge_core.cloud_session import CloudSessionFactory, CloudUser
+
+__all__ = ["CloudUser", "get_session", "require_tier", "session_middleware_dep"]
+
+_BYOK_CONFIG = Path.home() / ".config" / "circuitforge" / "llm.yaml"
+
+
+def _detect_byok() -> bool:
+    try:
+        import yaml
+        with open(_BYOK_CONFIG) as f:
+            cfg = yaml.safe_load(f) or {}
+        return any(
+            b.get("enabled", True) and b.get("type") != "vision_service"
+            for b in cfg.get("backends", {}).values()
+        )
+    except Exception:
+        return False
+
+
+_factory = CloudSessionFactory(
+    product="peregrine",
+    byok_detector=_detect_byok,
+)
+
+get_session = _factory.dependency()
+require_tier = _factory.require_tier
+
+
+def session_middleware_dep(request: Request, response: Response) -> None:
+    """Global FastAPI dependency — resolves the session and sets the request-scoped
+    user_id ContextVar so llm._allocate_orch_async can forward it to cf-orch.
+
+    Add as a global dependency in main.py:
+        app = FastAPI(..., dependencies=[Depends(session_middleware_dep)])
+    """
+    from app.llm import set_request_user_id
+
+    session = _factory.resolve(request, response)
+    user_id = session.user_id
+
+    # Only forward real cloud UUIDs — local/dev/anon sessions use the shared catalog
+    if user_id in (None, "local", "local-dev") or (user_id or "").startswith("anon-"):
+        user_id = None
+
+    set_request_user_id(user_id)
diff --git a/resume_matcher/apps/backend/app/llm.py b/resume_matcher/apps/backend/app/llm.py
new file mode 100644
index 0000000..a9dcbd7
--- /dev/null
+++ b/resume_matcher/apps/backend/app/llm.py
@@ -0,0 +1,811 @@
+"""LiteLLM wrapper for multi-provider AI support."""
+
+import json
+import logging
+import os
+import re
+from contextlib import asynccontextmanager
+from contextvars import ContextVar
+from dataclasses import dataclass
+from typing import Any
+
+import httpx
+import litellm
+from pydantic import BaseModel
+
+from app.config import settings
+
+# LLM timeout configuration (seconds) - base values
+LLM_TIMEOUT_HEALTH_CHECK = 30
+LLM_TIMEOUT_COMPLETION = 120
+LLM_TIMEOUT_JSON = 180  # JSON completions may take longer
+
+# LLM-004: OpenRouter JSON-capable models (explicit allowlist)
+OPENROUTER_JSON_CAPABLE_MODELS = {
+    # Anthropic models
+    "anthropic/claude-3-opus",
+    "anthropic/claude-3-sonnet",
+    "anthropic/claude-3-haiku",
+    "anthropic/claude-3.5-sonnet",
+    "anthropic/claude-3.5-haiku",
+    "anthropic/claude-haiku-4-5-20251001",
+    "anthropic/claude-sonnet-4-20250514",
+    "anthropic/claude-opus-4-20250514",
+    # OpenAI models
+    "openai/gpt-4-turbo",
+    "openai/gpt-4",
+    "openai/gpt-4o",
+    "openai/gpt-4o-mini",
+    "openai/gpt-3.5-turbo",
+    "openai/gpt-5-nano-2025-08-07",
+    # Google models
+    "google/gemini-pro",
+    "google/gemini-1.5-pro",
+    "google/gemini-1.5-flash",
+    "google/gemini-2.0-flash",
+    "google/gemini-3-flash-preview",
+    # DeepSeek models
+    "deepseek/deepseek-chat",
+    "deepseek/deepseek-reasoner",
+    # Mistral models
+    "mistralai/mistral-large",
+    "mistralai/mistral-medium",
+}
+
+# JSON-010: JSON extraction safety limits
+MAX_JSON_EXTRACTION_RECURSION = 10
+MAX_JSON_CONTENT_SIZE = 1024 * 1024  # 1MB
+
+# Request-scoped user_id — set once by session_middleware_dep, read inside _allocate_orch_async.
+# ContextVar is safe for concurrent async requests: each request task gets its own copy.
+_request_user_id: ContextVar[str | None] = ContextVar("request_user_id", default=None)
+
+
+def set_request_user_id(user_id: str | None) -> None:
+    _request_user_id.set(user_id)
+
+
+def get_request_user_id() -> str | None:
+    return _request_user_id.get()
+
+
+class LLMConfig(BaseModel):
+    """LLM configuration model."""
+
+    provider: str
+    model: str
+    api_key: str
+    api_base: str | None = None
+
+
+@dataclass
+class _OrchAllocation:
+    allocation_id: str
+    url: str
+    service: str
+
+
+@asynccontextmanager
+async def _allocate_orch_async(
+    coordinator_url: str,
+    service: str,
+    model_candidates: list[str],
+    ttl_s: float,
+    caller: str,
+):
+    """Async context manager that allocates a cf-orch service and releases on exit."""
+    async with httpx.AsyncClient(timeout=120.0) as client:
+        payload: dict[str, Any] = {
+            "model_candidates": model_candidates,
+            "ttl_s": ttl_s,
+            "caller": caller,
+        }
+        uid = get_request_user_id()
+        if uid:
+            payload["user_id"] = uid
+        resp = await client.post(
+            f"{coordinator_url.rstrip('/')}/api/services/{service}/allocate",
+            json=payload,
+        )
+        if not resp.is_success:
+            raise RuntimeError(
+                f"cf-orch allocation failed for {service!r}: "
+                f"HTTP {resp.status_code} — {resp.text[:200]}"
+            )
+        data = resp.json()
+        alloc = _OrchAllocation(
+            allocation_id=data["allocation_id"],
+            url=data["url"],
+            service=service,
+        )
+        try:
+            yield alloc
+        finally:
+            try:
+                await client.delete(
+                    f"{coordinator_url.rstrip('/')}/api/services/{service}/allocations/{alloc.allocation_id}",
+                    timeout=10.0,
+                )
+            except Exception as exc:
+                logging.debug("cf-orch release failed (non-fatal): %s", exc)
+
+
+def _normalize_api_base(provider: str, api_base: str | None) -> str | None:
+    """Normalize api_base for LiteLLM provider-specific expectations.
+
+    When using proxies/aggregators, users often paste a base URL that already
+    includes a version segment (e.g., `/v1`). Some LiteLLM provider handlers
+    append those segments internally, which can lead to duplicated paths like
+    `/v1/v1/...` and cause 404s.
+    """
+    if not api_base:
+        return None
+
+    base = api_base.strip()
+    if not base:
+        return None
+
+    base = base.rstrip("/")
+
+    # Anthropic handler appends '/v1/messages'. If base already ends with '/v1',
+    # strip it to avoid '/v1/v1/messages'.
+    if provider == "anthropic" and base.endswith("/v1"):
+        base = base[: -len("/v1")].rstrip("/")
+
+    # Gemini handler appends '/v1/models/...'. If base already ends with '/v1',
+    # strip it to avoid '/v1/v1/models/...'.
+    if provider == "gemini" and base.endswith("/v1"):
+        base = base[: -len("/v1")].rstrip("/")
+
+    return base or None
+
+
+def _extract_text_parts(value: Any, depth: int = 0, max_depth: int = 10) -> list[str]:
+    """Recursively extract text segments from nested response structures.
+
+    Handles strings, lists, dicts with 'text'/'content'/'value' keys, and objects
+    with text/content attributes. Limits recursion depth to avoid cycles.
+
+    Args:
+        value: Input value that may contain text in strings, lists, dicts, or objects.
+        depth: Current recursion depth.
+        max_depth: Maximum recursion depth before returning no content.
+
+    Returns:
+        A list of extracted text segments.
+    """
+    if depth >= max_depth:
+        return []
+
+    if value is None:
+        return []
+
+    if isinstance(value, str):
+        return [value]
+
+    if isinstance(value, list):
+        parts: list[str] = []
+        next_depth = depth + 1
+        for item in value:
+            parts.extend(_extract_text_parts(item, next_depth, max_depth))
+        return parts
+
+    if isinstance(value, dict):
+        next_depth = depth + 1
+        if "text" in value:
+            return _extract_text_parts(value.get("text"), next_depth, max_depth)
+        if "content" in value:
+            return _extract_text_parts(value.get("content"), next_depth, max_depth)
+        if "value" in value:
+            return _extract_text_parts(value.get("value"), next_depth, max_depth)
+        return []
+
+    next_depth = depth + 1
+    if hasattr(value, "text"):
+        return _extract_text_parts(getattr(value, "text"), next_depth, max_depth)
+    if hasattr(value, "content"):
+        return _extract_text_parts(getattr(value, "content"), next_depth, max_depth)
+
+    return []
+
+
+def _join_text_parts(parts: list[str]) -> str | None:
+    """Join text parts with newlines, filtering empty strings.
+
+    Args:
+        parts: Candidate text segments.
+
+    Returns:
+        Joined string or None if the result is empty.
+    """
+    joined = "\n".join(part for part in parts if part).strip()
+    return joined or None
+
+
+def _extract_message_text(message: Any) -> str | None:
+    """Extract plain text from a LiteLLM message object across providers."""
+    content: Any = None
+
+    if hasattr(message, "content"):
+        content = message.content
+    elif isinstance(message, dict):
+        content = message.get("content")
+
+    return _join_text_parts(_extract_text_parts(content))
+
+
+def _extract_choice_text(choice: Any) -> str | None:
+    """Extract plain text from a LiteLLM choice object.
+
+    Tries message.content first, then choice.text, then choice.delta. Handles both
+    object attributes and dict keys.
+
+    Args:
+        choice: LiteLLM choice object or dict.
+
+    Returns:
+        Extracted text or None if no content is found.
+    """
+    message: Any = None
+    if hasattr(choice, "message"):
+        message = choice.message
+    elif isinstance(choice, dict):
+        message = choice.get("message")
+
+    content = _extract_message_text(message)
+    if content:
+        return content
+
+    if hasattr(choice, "text"):
+        content = _join_text_parts(_extract_text_parts(getattr(choice, "text")))
+        if content:
+            return content
+    if isinstance(choice, dict) and "text" in choice:
+        content = _join_text_parts(_extract_text_parts(choice.get("text")))
+        if content:
+            return content
+
+    if hasattr(choice, "delta"):
+        content = _join_text_parts(_extract_text_parts(getattr(choice, "delta")))
+        if content:
+            return content
+    if isinstance(choice, dict) and "delta" in choice:
+        content = _join_text_parts(_extract_text_parts(choice.get("delta")))
+        if content:
+            return content
+
+    return None
+
+
+def _to_code_block(content: str | None, language: str = "text") -> str:
+    """Wrap content in a markdown code block for client display."""
+    text = (content or "").strip()
+    if not text:
+        text = "<empty>"
+    return f"```{language}\n{text}\n```"
+
+
+def _load_stored_config() -> dict:
+    """Load config from config.json file."""
+    config_path = settings.config_path
+    if config_path.exists():
+        try:
+            return json.loads(config_path.read_text())
+        except (json.JSONDecodeError, OSError):
+            return {}
+    return {}
+
+
+def get_llm_config() -> LLMConfig:
+    """Get current LLM configuration.
+
+    Priority: config.json file > environment variables/settings
+    """
+    stored = _load_stored_config()
+
+    return LLMConfig(
+        provider=stored.get("provider", settings.llm_provider),
+        model=stored.get("model", settings.llm_model),
+        api_key=stored.get("api_key", settings.llm_api_key),
+        api_base=stored.get("api_base", settings.llm_api_base),
+    )
+
+
+def get_model_name(config: LLMConfig) -> str:
+    """Convert provider/model to LiteLLM format.
+
+    For most providers, adds the provider prefix if not already present.
+    For OpenRouter, always adds 'openrouter/' prefix since OpenRouter models
+    use nested prefixes like 'openrouter/anthropic/claude-3.5-sonnet'.
+    """
+    provider_prefixes = {
+        "openai": "",  # OpenAI models don't need prefix
+        "anthropic": "anthropic/",
+        "openrouter": "openrouter/",
+        "gemini": "gemini/",
+        "deepseek": "deepseek/",
+        "ollama": "ollama/",
+    }
+
+    prefix = provider_prefixes.get(config.provider, "")
+
+    # OpenRouter is special: always add openrouter/ prefix unless already present
+    # OpenRouter models use nested format: openrouter/anthropic/claude-3.5-sonnet
+    if config.provider == "openrouter":
+        if config.model.startswith("openrouter/"):
+            return config.model
+        return f"openrouter/{config.model}"
+
+    # For other providers, don't add prefix if model already has a known prefix
+    known_prefixes = ["openrouter/", "anthropic/", "gemini/", "deepseek/", "ollama/"]
+    if any(config.model.startswith(p) for p in known_prefixes):
+        return config.model
+
+    # Add provider prefix for models that need it
+    return f"{prefix}{config.model}" if prefix else config.model
+
+
+def _supports_temperature(provider: str, model: str) -> bool:
+    """Return whether passing `temperature` is supported for this model/provider combo.
+
+    Some models (e.g., OpenAI gpt-5 family) reject temperature values other than 1,
+    and LiteLLM may error when temperature is passed.
+    """
+    _ = provider
+    model_lower = model.lower()
+    if "gpt-5" in model_lower:
+        return False
+    return True
+
+
+def _get_reasoning_effort(provider: str, model: str) -> str | None:
+    """Return a default reasoning_effort for models that require it.
+
+    Some OpenAI gpt-5 models may return empty message.content unless a supported
+    `reasoning_effort` is explicitly set. This keeps downstream JSON parsing reliable.
+    """
+    _ = provider
+    model_lower = model.lower()
+    if "gpt-5" in model_lower:
+        return "minimal"
+    return None
+
+
+async def check_llm_health(
+    config: LLMConfig | None = None,
+    *,
+    include_details: bool = False,
+    test_prompt: str | None = None,
+) -> dict[str, Any]:
+    """Check if the LLM provider is accessible and working."""
+    if config is None:
+        config = get_llm_config()
+
+    # Check if API key is configured (except for Ollama)
+    if config.provider != "ollama" and not config.api_key:
+        return {
+            "healthy": False,
+            "provider": config.provider,
+            "model": config.model,
+            "error_code": "api_key_missing",
+        }
+
+    model_name = get_model_name(config)
+
+    prompt = test_prompt or "Hi"
+
+    try:
+        # Make a minimal test call with timeout
+        # Pass API key directly to avoid race conditions with global os.environ
+        kwargs: dict[str, Any] = {
+            "model": model_name,
+            "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": 16,
+            "api_key": config.api_key,
+            "api_base": _normalize_api_base(config.provider, config.api_base),
+            "timeout": LLM_TIMEOUT_HEALTH_CHECK,
+        }
+        reasoning_effort = _get_reasoning_effort(config.provider, model_name)
+        if reasoning_effort:
+            kwargs["reasoning_effort"] = reasoning_effort
+
+        response = await litellm.acompletion(**kwargs)
+        content = _extract_choice_text(response.choices[0])
+        if not content:
+            # LLM-003: Empty response should mark health check as unhealthy
+            logging.warning(
+                "LLM health check returned empty content",
+                extra={"provider": config.provider, "model": config.model},
+            )
+            result: dict[str, Any] = {
+                "healthy": False,  # Fixed: empty content means unhealthy
+                "provider": config.provider,
+                "model": config.model,
+                "response_model": response.model if response else None,
+                "error_code": "empty_content",  # Changed from warning_code
+                "message": "LLM returned empty response",
+            }
+            if include_details:
+                result["test_prompt"] = _to_code_block(prompt)
+                result["model_output"] = _to_code_block(None)
+            return result
+
+        result = {
+            "healthy": True,
+            "provider": config.provider,
+            "model": config.model,
+            "response_model": response.model if response else None,
+        }
+        if include_details:
+            result["test_prompt"] = _to_code_block(prompt)
+            result["model_output"] = _to_code_block(content)
+        return result
+    except Exception as e:
+        # Log full exception details server-side, but do not expose them to clients
+        logging.exception(
+            "LLM health check failed",
+            extra={"provider": config.provider, "model": config.model},
+        )
+
+        # Provide a minimal, actionable client-facing hint without leaking secrets.
+        error_code = "health_check_failed"
+        message = str(e)
+        if "404" in message and "/v1/v1/" in message:
+            error_code = "duplicate_v1_path"
+        elif "404" in message:
+            error_code = "not_found_404"
+        elif "<!doctype html" in message.lower() or "<html" in message.lower():
+            error_code = "html_response"
+        result = {
+            "healthy": False,
+            "provider": config.provider,
+            "model": config.model,
+            "error_code": error_code,
+        }
+        if include_details:
+            result["test_prompt"] = _to_code_block(prompt)
+            result["model_output"] = _to_code_block(None)
+            result["error_detail"] = _to_code_block(message)
+        return result
+
+
+async def complete(
+    prompt: str,
+    system_prompt: str | None = None,
+    config: LLMConfig | None = None,
+    max_tokens: int = 4096,
+    temperature: float = 0.7,
+) -> str:
+    """Make a completion request to the LLM."""
+    if config is None:
+        cf_orch_url = os.environ.get("CF_ORCH_URL", "").strip()
+        if cf_orch_url:
+            try:
+                async with _allocate_orch_async(
+                    cf_orch_url,
+                    "vllm",
+                    model_candidates=["Qwen2.5-3B-Instruct"],
+                    ttl_s=300.0,
+                    caller="peregrine-resume-matcher",
+                ) as alloc:
+                    orch_config = LLMConfig(
+                        provider="openai",
+                        model="__auto__",
+                        api_key="any",
+                        api_base=alloc.url.rstrip("/") + "/v1",
+                    )
+                    return await complete(prompt, system_prompt, orch_config, max_tokens, temperature)
+            except Exception as exc:
+                logging.warning("cf-orch allocation failed, falling back to default config: %s", exc)
+        config = get_llm_config()
+
+    model_name = get_model_name(config)
+
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    messages.append({"role": "user", "content": prompt})
+
+    try:
+        # Pass API key directly to avoid race conditions with global os.environ
+        kwargs: dict[str, Any] = {
+            "model": model_name,
+            "messages": messages,
+            "max_tokens": max_tokens,
+            "api_key": config.api_key,
+            "api_base": _normalize_api_base(config.provider, config.api_base),
+            "timeout": LLM_TIMEOUT_COMPLETION,
+        }
+        if _supports_temperature(config.provider, model_name):
+            kwargs["temperature"] = temperature
+        reasoning_effort = _get_reasoning_effort(config.provider, model_name)
+        if reasoning_effort:
+            kwargs["reasoning_effort"] = reasoning_effort
+
+        response = await litellm.acompletion(**kwargs)
+
+        content = _extract_choice_text(response.choices[0])
+        if not content:
+            raise ValueError("Empty response from LLM")
+        return content
+    except Exception as e:
+        # Log the actual error server-side for debugging
+        logging.error(f"LLM completion failed: {e}", extra={"model": model_name})
+        raise ValueError(
+            "LLM completion failed. Please check your API configuration and try again."
+        ) from e
+
+
+def _supports_json_mode(provider: str, model: str) -> bool:
+    """Check if the model supports JSON mode."""
+    # Models that support response_format={"type": "json_object"}
+    json_mode_providers = ["openai", "anthropic", "gemini", "deepseek"]
+    if provider in json_mode_providers:
+        return True
+    # LLM-004: OpenRouter models - use explicit allowlist instead of substring matching
+    if provider == "openrouter":
+        return model in OPENROUTER_JSON_CAPABLE_MODELS
+    return False
+
+
+def _appears_truncated(data: dict) -> bool:
+    """LLM-001: Check if JSON data appears to be truncated.
+
+    Detects suspicious patterns indicating incomplete responses.
+    """
+    if not isinstance(data, dict):
+        return False
+
+    # Check for empty arrays that should typically have content
+    suspicious_empty_arrays = ["workExperience", "education", "skills"]
+    for key in suspicious_empty_arrays:
+        if key in data and data[key] == []:
+            # Log warning - these are rarely empty in real resumes
+            logging.warning(
+                "Possible truncation detected: '%s' is empty",
+                key,
+            )
+            return True
+
+    # Check for missing critical sections
+    required_top_level = ["personalInfo"]
+    for key in required_top_level:
+        if key not in data:
+            logging.warning(
+                "Possible truncation detected: missing required section '%s'",
+                key,
+            )
+            return True
+
+    return False
+
+
+def _get_retry_temperature(attempt: int, base_temp: float = 0.1) -> float:
+    """LLM-002: Get temperature for retry attempt - increases with each retry.
+
+    Higher temperature on retries gives the model more variation to produce
+    different (hopefully valid) output.
+    """
+    temperatures = [base_temp, 0.3, 0.5, 0.7]
+    return temperatures[min(attempt, len(temperatures) - 1)]
+
+
+def _calculate_timeout(
+    operation: str,
+    max_tokens: int = 4096,
+    provider: str = "openai",
+) -> int:
+    """LLM-005: Calculate adaptive timeout based on operation and parameters."""
+    base_timeouts = {
+        "health_check": LLM_TIMEOUT_HEALTH_CHECK,
+        "completion": LLM_TIMEOUT_COMPLETION,
+        "json": LLM_TIMEOUT_JSON,
+    }
+
+    base = base_timeouts.get(operation, LLM_TIMEOUT_COMPLETION)
+
+    # Scale by token count (relative to 4096 baseline)
+    token_factor = max(1.0, max_tokens / 4096)
+
+    # Provider-specific latency adjustments
+    provider_factors = {
+        "openai": 1.0,
+        "anthropic": 1.2,
+        "openrouter": 1.5,  # More variable latency
+        "ollama": 2.0,  # Local models can be slower
+    }
+    provider_factor = provider_factors.get(provider, 1.0)
+
+    return int(base * token_factor * provider_factor)
+
+
+def _extract_json(content: str, _depth: int = 0) -> str:
+    """Extract JSON from LLM response, handling various formats.
+
+    LLM-001: Improved to detect and reject likely truncated JSON.
+    LLM-007: Improved error messages for debugging.
+    JSON-010: Added recursion depth and size limits.
+    """
+    # JSON-010: Safety limits
+    if _depth > MAX_JSON_EXTRACTION_RECURSION:
+        raise ValueError(f"JSON extraction exceeded max recursion depth: {_depth}")
+    if len(content) > MAX_JSON_CONTENT_SIZE:
+        raise ValueError(f"Content too large for JSON extraction: {len(content)} bytes")
+
+    original = content
+
+    # Remove markdown code blocks
+    if "```json" in content:
+        content = content.split("```json")[1].split("```")[0]
+    elif "```" in content:
+        parts = content.split("```")
+        if len(parts) >= 2:
+            content = parts[1]
+            # Remove language identifier if present (e.g., "json\n{...")
+            if content.startswith(("json", "JSON")):
+                content = content[4:]
+
+    content = content.strip()
+
+    # If content starts with {, find the matching }
+    if content.startswith("{"):
+        depth = 0
+        end_idx = -1
+        in_string = False
+        escape_next = False
+
+        for i, char in enumerate(content):
+            if escape_next:
+                escape_next = False
+                continue
+            if char == "\\":
+                escape_next = True
+                continue
+            if char == '"' and not escape_next:
+                in_string = not in_string
+                continue
+            if in_string:
+                continue
+            if char == "{":
+                depth += 1
+            elif char == "}":
+                depth -= 1
+                if depth == 0:
+                    end_idx = i
+                    break
+
+        # LLM-001: Check for unbalanced braces - loop ended without depth reaching 0
+        if end_idx == -1 and depth != 0:
+            logging.warning(
+                "JSON extraction found unbalanced braces (depth=%d), possible truncation",
+                depth,
+            )
+
+        if end_idx != -1:
+            return content[: end_idx + 1]
+
+    # Try to find JSON object in the content (only if not already at start)
+    start_idx = content.find("{")
+    if start_idx > 0:
+        # Only recurse if { is found after position 0 to avoid infinite recursion
+        return _extract_json(content[start_idx:], _depth + 1)
+
+    # LLM-007: Log unrecognized format for debugging
+    logging.error(
+        "Could not extract JSON from response format. Content preview: %s",
+        content[:200] if content else "<empty>",
+    )
+    raise ValueError(f"No JSON found in response: {original[:200]}")
+
+
+async def complete_json(
+    prompt: str,
+    system_prompt: str | None = None,
+    config: LLMConfig | None = None,
+    max_tokens: int = 4096,
+    retries: int = 2,
+) -> dict[str, Any]:
+    """Make a completion request expecting JSON response.
+
+    Uses JSON mode when available, with retry logic for reliability.
+    """
+    if config is None:
+        cf_orch_url = os.environ.get("CF_ORCH_URL", "").strip()
+        if cf_orch_url:
+            try:
+                async with _allocate_orch_async(
+                    cf_orch_url,
+                    "vllm",
+                    model_candidates=["Qwen2.5-3B-Instruct"],
+                    ttl_s=300.0,
+                    caller="peregrine-resume-matcher",
+                ) as alloc:
+                    orch_config = LLMConfig(
+                        provider="openai",
+                        model="__auto__",
+                        api_key="any",
+                        api_base=alloc.url.rstrip("/") + "/v1",
+                    )
+                    return await complete_json(prompt, system_prompt, orch_config, max_tokens, retries)
+            except Exception as exc:
+                logging.warning("cf-orch allocation failed, falling back to default config: %s", exc)
+        config = get_llm_config()
+
+    model_name = get_model_name(config)
+
+    # Build messages
+    json_system = (
+        system_prompt or ""
+    ) + "\n\nYou must respond with valid JSON only. No explanations, no markdown."
+    messages = [
+        {"role": "system", "content": json_system},
+        {"role": "user", "content": prompt},
+    ]
+
+    # Check if we can use JSON mode
+    use_json_mode = _supports_json_mode(config.provider, config.model)
+
+    last_error = None
+    for attempt in range(retries + 1):
+        try:
+            # Build request kwargs
+            # Pass API key directly to avoid race conditions with global os.environ
+            kwargs: dict[str, Any] = {
+                "model": model_name,
+                "messages": messages,
+                "max_tokens": max_tokens,
+                "api_key": config.api_key,
+                "api_base": _normalize_api_base(config.provider, config.api_base),
+                "timeout": _calculate_timeout("json", max_tokens, config.provider),
+            }
+            if _supports_temperature(config.provider, model_name):
+                # LLM-002: Increase temperature on retry for variation
+                kwargs["temperature"] = _get_retry_temperature(attempt)
+            reasoning_effort = _get_reasoning_effort(config.provider, model_name)
+            if reasoning_effort:
+                kwargs["reasoning_effort"] = reasoning_effort
+
+            # Add JSON mode if supported
+            if use_json_mode:
+                kwargs["response_format"] = {"type": "json_object"}
+
+            response = await litellm.acompletion(**kwargs)
+            content = _extract_choice_text(response.choices[0])
+
+            if not content:
+                raise ValueError("Empty response from LLM")
+
+            logging.debug(f"LLM response (attempt {attempt + 1}): {content[:300]}")
+
+            # Extract and parse JSON
+            json_str = _extract_json(content)
+            result = json.loads(json_str)
+
+            # LLM-001: Check if parsed result appears truncated
+            if isinstance(result, dict) and _appears_truncated(result):
+                logging.warning(
+                    "Parsed JSON appears truncated, but proceeding with result"
+                )
+
+            return result
+
+        except json.JSONDecodeError as e:
+            last_error = e
+            logging.warning(f"JSON parse failed (attempt {attempt + 1}): {e}")
+            if attempt < retries:
+                # Add hint to prompt for retry
+                messages[-1]["content"] = (
+                    prompt
+                    + "\n\nIMPORTANT: Output ONLY a valid JSON object. Start with { and end with }."
+                )
+                continue
+            raise ValueError(f"Failed to parse JSON after {retries + 1} attempts: {e}")
+
+        except Exception as e:
+            last_error = e
+            logging.warning(f"LLM call failed (attempt {attempt + 1}): {e}")
+            if attempt < retries:
+                continue
+            raise
+
+    raise ValueError(f"Failed after {retries + 1} attempts: {last_error}")
diff --git a/resume_matcher/apps/backend/app/main.py b/resume_matcher/apps/backend/app/main.py
new file mode 100644
index 0000000..d053c2c
--- /dev/null
+++ b/resume_matcher/apps/backend/app/main.py
@@ -0,0 +1,88 @@
+"""FastAPI application entry point."""
+
+import asyncio
+import logging
+import sys
+from contextlib import asynccontextmanager
+
+from fastapi import Depends, FastAPI
+
+# Fix for Windows: Use ProactorEventLoop for subprocess support (Playwright)
+if sys.platform == "win32":
+    asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy())
+
+logger = logging.getLogger(__name__)
+from fastapi.middleware.cors import CORSMiddleware
+
+from app import __version__
+from app.cloud_session import session_middleware_dep
+from app.config import settings
+from app.database import db
+from app.pdf import close_pdf_renderer, init_pdf_renderer
+from app.routers import config_router, enrichment_router, health_router, jobs_router, resumes_router
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Application lifespan manager."""
+    # Startup
+    settings.data_dir.mkdir(parents=True, exist_ok=True)
+    # PDF renderer uses lazy initialization - will initialize on first use
+    # await init_pdf_renderer()
+    yield
+    # Shutdown - wrap each cleanup in try-except to ensure all resources are released
+    try:
+        await close_pdf_renderer()
+    except Exception as e:
+        logger.error(f"Error closing PDF renderer: {e}")
+
+    try:
+        db.close()
+    except Exception as e:
+        logger.error(f"Error closing database: {e}")
+
+
+app = FastAPI(
+    title="Resume Matcher API",
+    description="AI-powered resume tailoring for job descriptions",
+    version=__version__,
+    lifespan=lifespan,
+    dependencies=[Depends(session_middleware_dep)],
+)
+
+# CORS middleware - origins configurable via CORS_ORIGINS env var
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.cors_origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Include routers
+app.include_router(health_router, prefix="/api/v1")
+app.include_router(config_router, prefix="/api/v1")
+app.include_router(resumes_router, prefix="/api/v1")
+app.include_router(jobs_router, prefix="/api/v1")
+app.include_router(enrichment_router, prefix="/api/v1")
+
+
+@app.get("/")
+async def root():
+    """Root endpoint."""
+    return {
+        "name": "Resume Matcher API",
+        "version": __version__,
+        "docs": "/docs",
+    }
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(
+        "app.main:app",
+        host=settings.host,
+        port=settings.port,
+        reload=True,
+    )