AI Engineer

Job Description

Job Description

Job Title: AI Engineer
Location: Richardson, TX 75082 (Onsite)
Duration: 12 Months

Experience: 7 years overall, with 5 years in AI/ML development

Must-Have Skills:

• Python 3.11, advanced type hints, async programming
• LangGraph and LangChain
• Azure OpenAI / OpenAI APIs
• FastAPI, REST APIs
• Vector Databases Qdrant and/or Milvus
• RAG, prompt engineering, grounding, agent development
• PostgreSQL 16/17, SQLAlchemy, Alembic, advanced SQL
• Docker and Linux
• Redis, Pydantic, Pandas, PyArrow
• ETL/ELT and Prefect 2.x/3.x
• OAuth 2.0, MSAL and enterprise API integrations
• Microsoft Graph API SharePoint, Outlook, Planner, OneDrive
• ServiceNow REST API and Splunk SDK

AI / LLM / RAG:

• Build and maintain LLM/AI agents using LangGraph and LangChain
• ReAct and tool-based agent architectures
• Azure OpenAI/OpenAI integration, prompt engineering and structured outputs
• RAG pipelines, embeddings, hybrid search and reranking
• Vector search using Qdrant/Milvus, including HNSW indexing and filtering
• RAG evaluation using Faithfulness, Relevance, NDCG and MRR
• LLM fine-tuning and neural network/ML model development
• Guardrails, PII redaction, memory, fallback and error-recovery patterns
• Langfuse for tracing, evaluation and prompt management

Data Engineering / ETL:

• Design ETL/ELT pipelines using Prefect
• PostgreSQL, advanced SQL, joins, CTEs and window functions
• Pandas, PyArrow and columnar data processing
• Azure Blob Storage
• Document ingestion/parsing using Docling, Unstructured, python-docx and python-pptx
• Schema evolution, query optimization and warehouse/schema management

API & Enterprise Integrations:

• OAuth 2.0 client-credentials flow and token lifecycle
• MSAL and Azure AD app registrations
• Microsoft Graph API integrations with SharePoint, Outlook, Planner and OneDrive
• ServiceNow REST/Table APIs, incident/change management and bulk operations
• Splunk SDK, saved searches, async queries and log analysis
• Pagination and application permissions

DevOps / Platform:

• Docker and Linux fundamentals
• Async/concurrent processing, retry/backoff and structured logging/tracing
• Redis pub/sub and TTL
• Nice to have: Ray/distributed execution

Nice-to-Have:

• Columnar performance tuning
• Network operations / NOC / alarm correlation knowledge
• IPAM / OTNA integrations

1. RAG & LLM Engineering Build RAG solutions, agent workflows, prompt grounding and LLM fine-tuning.
2. Enterprise API Integrations Develop OAuth 2.0/MSAL integrations with Microsoft Graph, SharePoint, Outlook, Planner, OneDrive, ServiceNow and Splunk.
3. Vector Search Design and optimize Qdrant/Milvus-based vector databases, embeddings, retrieval, filtering and reranking.