Senior AI Engineer – Generative AI, RAG and Cloud

Position: Senior AI/ML Engineer – Generative AI, RAG and Cloud

Location:

Remote

Type:

Full-time

Clearance:

No clearance anticipated; background check and federal credentialing required.

Position Summary

seeking a hands-on Senior AI/ML Engineer to support artificial-intelligence initiatives:

• Develop a RAG-based research assistant using scientific publications, agency documents, and other curated information.
• Add semantic search and future AI-agent capabilities to an existing scientific platform being migrated to Azure.

The ideal candidate has personally built LLM/RAG applications and deployed AI or ML solutions in a public-cloud environment. This is a hands-on development role requiring strong Python, software-engineering, and cloud skills.

Responsibilities

• Design and implement RAG and LLM-based applications.
• Build document-ingestion, chunking, embedding, vector-search, and retrieval pipelines.
• Develop grounded responses with citations and hallucination controls.
• Implement semantic search across scientific and technical information.
• Build APIs and integrate AI capabilities into existing applications.
• Deploy and support AI services in Azure or AWS.
• Help develop AI agents and automated workflows in later project phases.
• Collaborate with ML engineers, application developers, cloud teams, and customer stakeholders.

Required Qualifications

• Strong Python and software-development experience.
• Hands-on experience building RAG and LLM applications.
• Experience with embeddings, vector databases, document retrieval, and semantic search.
• Experience integrating AI services through APIs.
• Experience deploying AI, ML, or data applications in Azure or AWS.
• Understanding of grounding, citations, evaluation, and hallucination reduction.
• Strong communication and collaboration skills.

Preferred Qualifications

• Experience with Azure OpenAI, Azure AI Search, AWS Bedrock, or comparable services.
• Experience building production or enterprise-scale RAG solutions.
• Experience with AI agents and workflow automation.
• Experience adding AI capabilities to existing or legacy applications.
• Experience with scientific, biomedical, chemical, regulatory, or other technical documents.
• Federal-government or NIH experience.

Recruiting Priority

Prioritize candidates who have personally built and deployed a working RAG solution. Azure is preferred, but strong AWS experience is acceptable. Scientific-domain experience is helpful but not required.

Avoid candidates whose experience is limited to AI strategy, prompt engineering, traditional ML without LLM/RAG, or cloud infrastructure without hands-on AI application development.