Title: Sr AI/LLM Engineer
Location: Irvine, CA (Onsite)
Duration: 6 months (possibility of an extension)
Implementation Partner: Infosys
End Client: To be disclosed
JD:
Overall 8+ years' experience with 5+ years in AI development. PFB the technology skills required. Core Language & Architecture Python 3.11+ Advanced type hints (PEP 484), static typing discipline Async programming (asyncio, async/await, async generators) aiohttp / httpx (async HTTP clients) Pydantic v2 (BaseModel, validation, settings management) Structured logging & tracing patterns Redis (pub/sub, TTL, async clients) REST API design & integration patterns Retry/backoff strategies (Tenacity) Concurrency patterns (parallel tool calls, task orchestration) AI / LLM / Agent Systems LangGraph (state machines, conditional edges, checkpointing) LangChain 0.3.x (LLMChain, StructuredTool, retrievers, prompt templates) ReAct-style agent architectures Tool-based agent design (40+ tool environments) Azure OpenAI / OpenAI APIs (GPT-4o, deployment mgmt, rate limits, token budgeting) Prompt engineering (few-shot, structured output, JSON mode) PydanticOutputParser / structured LLM responses Guardrails / PII redaction patterns Memory abstractions for agents Langfuse (trace instrumentation, evaluation, prompt management) LLM fallback chains & error recovery RAG prompt grounding strategies LLM fine-tuning Neural Network training & tuning Traditional ML models (random forest, k-means clustering, linear regression, etc.) MCP development and consumption Retrieval, Search & RAG Engineering Vector databases (Qdrant and/or Milvus) HNSW indexing parameters Filtering strategies Embedding pipelines (OpenAI ada-002 or equivalent) Batch embedding & re-indexing workflows Hybrid retrieval (BM25 + semantic) Score fusion strategies Cross-encoder reranking (BAAI/bge models) FastAPI-based inference services LangChain retriever abstractions RAG evaluation metrics: Faithfulness Relevance NDCG MRR Trace-level RAG evaluation (Langfuse) Data Engineering & ETL Prefect 2.x / 3.x Flows, tasks, futures Deployments (YAML) Scheduling ETL/ELT design Schema evolution Query optimization OAuth authentication Warehouse/schema management PostgreSQL 16/17 psycopg 3.x Connection pooling SQLAlchemy 2.x (ORM + asyncio) Alembic migrations Advanced SQL Multi-table JOINs CTEs Window functions Timezone conversion Pandas 2.x (complex multi-stage transformations) PyArrow / columnar formats Azure Blob Storage (azure-storage-blob) Document ingestion/parsing: Docling Unstructured python-docx python-pptx DevOps & Platform Docker Linux fundamentals Nice-to-Haves Ray (distributed execution) Columnar performance tuning Network operations domain knowledge NOC / alarm correlation familiarity API & Enterprise Integrations OAuth 2.0 (client credentials flow, token lifecycle) MSAL (browser + service principal flows) Microsoft Graph API SharePoint Outlook Planner OneDrive Pagination App permissions ServiceNow REST API Table API Incident/change mgmt Bulk operations Splunk SDK Saved searches Async queries Log analysis Azure AD app registrations IPAM / OTNA integrations (nice-to-have domain exposure)