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)
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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)
