Title:Β AI Engineer
Duration:Β 3 Months - Long Term
Location:Β Washington, DC
Contract: W2/1099
Certifications mandatory
Hybrid Onsite:Β 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon.
Role Overview
We are looking for a senior AI Engineer to design, build, and scale enterprise-grade GenAI solutions. The role focuses on RAG pipelines, agentic AI systems, and multi-cloud (Azure + AWS) deployments with strong software engineering fundamentals.
Key Responsibilities
Architect and build RAG-based AI solutions (Azure AI/Search, vector DBs, embeddings, ranking, evaluation).
Develop enterprise conversational AI systems with guardrails, telemetry, and audit logging.
Implement agentic AI frameworks (Semantic Kernel, AutoGen, CrewAI, LangChain) with secure tool orchestration.
Integrate Model Context Protocol (MCP) servers and manage AI infrastructure.
Build and manage data pipelines (ETL/ELT) for AI systems using ADF, Databricks, and AWS EMR.
Deploy scalable AI workloads using Azure & AWS services (AKS, SageMaker, Bedrock, etc.).
Perform model evaluation, fine-tuning, and optimization (quality, latency, cost, safety).
Ensure secure, scalable, and production-grade software engineering practices.
Required Skills:
Strong experience in GenAI/LLMs (RAG, agents, evaluation, safety)
Proficiency in Python, C#, .NET (TypeScript is a plus)
Hands-on with Azure & AWS AI/ML stack
Experience with vector databases, embeddings, and semantic search
Knowledge of agent orchestration, MCP, and AI governance
Solid CS fundamentals & system design
Experience & Education:
6+ years in software engineering
2+ years in GenAI/LLM-based solutions
Bachelors in CS/Engineering or equivalent experience
Certifications (Mandatory):
Azure AI Fundamentals (AI-900)
Azure Data Fundamentals (DP-900)
AWS ML Specialty
TensorFlow Developer
Kubernetes (CKA/CKAD)
Responsible AI + SAFe Agile ASE
Nice to Have:
Azure AI Engineer (AI-102), Data Scientist (DP-100), Architect (AZ-305), Developer (AZ-204)
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