AI Engineer

Overall 8+ years’ experience with 5+ years in AI development. PFB the technology skills required.

Candidate can work from any DC office. Open all over US

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)
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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
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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:
o Faithfulness
o Relevance
o NDCG
o MRR
• Trace-level RAG evaluation (Langfuse)
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Data Engineering & ETL
• Prefect 2.x / 3.x
o Flows, tasks, futures
o Deployments (YAML)
o Scheduling
• ETL/ELT design
o Schema evolution
o Query optimization
• OAuth authentication
• Warehouse/schema management
• PostgreSQL 16/17
o psycopg 3.x
o Connection pooling
o SQLAlchemy 2.x (ORM + asyncio)
o Alembic migrations
o Advanced SQL
o Multi-table JOINs
o CTEs
o Window functions
• Timezone conversion
• Pandas 2.x (complex multi-stage transformations)
• PyArrow / columnar formats
• Azure Blob Storage (azure-storage-blob)
• Document ingestion/parsing:
o Docling
o Unstructured
o python-docx
o python-pptx

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DevOps & Platform
• Docker
• Linux fundamentals

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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)

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