Senior Data Scientist / AI Engineer (Contract-to-Hire | Remote)

Senior Data Scientist / AI Engineer (Contract-to-Hire | Remote)
Duration: 1-year contract-to-hire
Location: Remote

If you’ve been building GenAI agents beyond demos—shipping them into production with real tool access, guardrails, and monitoring—this role is for you. We’re looking for a Senior Data Scientist / AI Engineer to build production-grade GenAI agents, MCP integrations, and enterprise knowledge/retrieval systems using a modern cloud and data stack.

What you’ll work on

• Build and refine GenAI agents that safely interact with internal systems and workflows

• Develop MCP tools and secure tool integrations (auth, permissions, auditing)

• Create and maintain knowledge bases, vector search, and retrieval pipelines (RAG)

• Implement agent eval frameworks, guardrails, and safety layers at scale

• Partner across Azure, Snowflake, and Databricks to support production AI systems

What we’re looking for

• 5+ years overall experience as a Data Scientist / AI Engineer

• Proven experience building and deploying GenAI agents in production (including open-source LLM agents)

• Strong knowledge of MCP, tool integrations, and agent orchestration

• Experience with RAG/knowledge systems: vector search, retrieval, embeddings, and KB design

• Comfortable with rapid prototyping (“vibe coding”) without sacrificing production quality

• Cloud experience: Azure preferred (AWS acceptable)

• Strong SQL and hands-on Snowflake experience

• Databricks experience for data/ML workflows

• Solid traditional ML foundation (classification, clustering, etc.)

• Experience building evals, guardrails, and LLM safety controls

• Familiarity with MLOps and LLM observability (tracing, cost/latency monitoring, drift, quality metrics)

Why this is interesting

You’ll be building the “real” version of GenAI—agents that can take action, backed by trustworthy retrieval, measurable quality, and enterprise-grade security.

Interested? Apply with your resume and include a quick note on:

1. A production agent you shipped (tools + architecture), and

2. Your experience with MCP, RAG, and eval/guardrails.