AI Engineer
eTeam Inc
Richardson, TX, United States
6 days ago
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Role details
Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
Tech stack
LangGraph Framework
Application Programming Interfaces (APIs)
Artificial Intelligence
Artificial Neural Networks
Microsoft Azure
Microsoft Online Services
Microsoft Outlook
Cloud Storage
Concurrent Computing
Information Engineering
Extract Transform Load (ETL)
Data Masking
+42 more
Linux
DevOps
Information Retrieval
Python (Programming Language)
PostgreSQL
Log Analysis
Machine Learning
OAuth
Performance Tuning
Query Optimization
Redis
OpenAI
Azure Active Directory
Search Technologies
Microsoft SharePoint
SQLAlchemy
Systems Integration
Data Logging
Data Processing
Qdrant
Computer Network Operations
Sql Optimization
Normalized Discounted Cumulative Gain
ReactJS
Retrieval-Augmented Generation
Transfer Learning
Large Language Models
Prompt Engineering
HNSW (Hierarchical Navigable Small World)
Langfuse β LLM Observability and Analytics Platform
Agentic-AI
Fastapi
Microsoft Onedrive
Pandas
Milvus
Restful APIs
Pagination
Splunk
Data Pipelines
Api Management
Docker
Servicenow
Job description
- 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
Top 3 Responsibilities
- RAG & LLM Engineering Build RAG solutions, agent workflows, prompt grounding and LLM fine-tuning.
- Enterprise API Integrations Develop OAuth 2.0/MSAL integrations with Microsoft Graph, SharePoint, Outlook, Planner, OneDrive, ServiceNow and Splunk.
- Vector Search Design and optimize Qdrant/Milvus-based vector databases, embeddings, retrieval, filtering and reranking.
Requirements
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
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