Agentic AI / Machine Learning Consultant

Summary

Vertical Relevance is seeking an Agentic AI/Machine Learning Consultant to join its team and deliver impactful client outcomes. The role is responsible for designing, developing, deploying, and monitoring advanced ML/AI solutions, while advising clients, collaborating with cross-functional teams, and contributing to technical thought leadership.

Responsibilities

• Deliver end-to-end ML/AI solutions from problem framing through deployment and monitoring
• Design scalable AI/ML architectures using AWS and GCP services
• Optimize RAG pipelines including chunking, embeddings, hybrid search, and re-ranking
• Develop retrieval evaluation frameworks (Recall@K, Precision@K, MRR, nDCG)
• Design and implement knowledge graphs and ontologies
• Build data ingestion pipelines for structured and unstructured data
• Deploy solutions using AWS tools such as SageMaker, Bedrock, Lambda, and OpenSearch
• Implement self-hosted embedding models in secure environments
• Ensure compliance with PHI/PII data security requirements
• Collaborate with cross-functional teams and act as a technical advisor
• Mentor team members and contribute to technical thought leadership

Skills

• Experience building and deploying ML/AI solutions in AWS, GCP, or Azure
• Strong expertise in RAG systems and retrieval optimization
• Experience with vector databases and hybrid search techniques
• Knowledge of knowledge graph design and entity resolution
• Proficiency in Python (R is a plus)
• Experience with LLM platforms such as OpenAI, Anthropic, or Gemini
• Strong communication and client-facing skills
• Experience with Amazon Neptune or other graph databases
• Familiarity with information retrieval metrics
• Experience with self-hosted embeddings
• Healthcare domain knowledge (payer/claims)
• Experience with agentic AI frameworks such as LangGraph or MCP

Qualifications

Must Haves

• Experience building and deploying ML/AI solutions in AWS, GCP, or Azure
• Strong expertise in RAG systems and retrieval optimization
• Experience with vector databases and hybrid search techniques
• Knowledge of knowledge graph design and entity resolution
• Proficiency in Python (R is a plus)
• Experience with LLM platforms such as OpenAI, Anthropic, or Gemini
• Strong communication and client-facing skills

Nice to Haves

• Experience with Amazon Neptune or other graph databases
• Familiarity with information retrieval metrics
• Experience with self-hosted embeddings
• Healthcare domain knowledge (payer/claims)
• Experience with agentic AI frameworks such as LangGraph or MCP