Responsibilities: Design and architect end-to-end GenAI solutions using LLMs (OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, etc.). Lead the implementation of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and agentic AI systems.
Numbers & Facts
Location
Mc Lean, VA
Description
Our Client, an IT Services and Consultant company, is looking for a Generative AI Solutions Engineer (LLM, RAG, AWS) for their Mc Lean, VA location.
Responsibilities:
Design and architect end-to-end GenAI solutions using LLMs (OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, etc.)
Lead the implementation of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and agentic AI systems
Define AI architecture patterns, best practices, and governance frameworks
Build scalable pipelines for data ingestion, vectorization (embeddings), and semantic search
Collaborate with business stakeholders to identify AI use cases and translate them into technical solutions
Implement and manage AI model deployment on cloud platforms (Azure, AWS, GCP)
Ensure responsible AI practices, including security, compliance, and bias mitigation
Integrate GenAI solutions with enterprise systems (APIs, microservices)
Mentor engineering teams and drive AI adoption roadmap
Monitor solution performance and continuously improve accuracy, latency, and cost efficiency Technical Skills
Requirements:
Strong experience in Python and familiarity with ML/AI frameworks
Knowledge of containerization (Docker, Kubernetes
Experience with REST APIs, microservices architecture, and system design
Familiarity with DevOps/MLOps practices (CI/CD, model monitoring, versioning)