Note: The job is a remote job and is open to candidates in USA. Transcarent is the One Place for Health and Care, bringing together medical, pharmacy, and point solutions with a generative AI-powered health and care platform. The Senior ML Engineer will design, build, tune, and evaluate multi-agentic systems that guide users through complex conversations, focusing on agent behavior and model selection.
Responsibilities
- Design and orchestrate multi-agentic workflows
- Own context engineering for production agents, including system design, safety rules, context injection, and clarifying question strategies
- Design tools and function-calling interfaces, so agents take reliable, well-structured actions
- Build and tune retrieval (RAG) pipelines—embeddings, vector search, filtering, query rewriting, and relevance tuning
- Select and optimize models across providers, balance quality, latency, determinism, and cost
- Design agent memory and context management for coherent multi-turn behavior
- Build safety and guardrail layers for input filtering, scope and safety checks, and graceful handling of edge cases
- Own LLM evaluation, offline eval suites, graders/LLM-as-judge, test sets and personas, metrics, and quality gates
- Collaborate with cross-functional stakeholders on requirements, project execution and status tracking
- Meta technical responsibility: Document high-fidelity technical designs, establish alignment on solutions within broader engineering team
Skills
- Bachelor's or master's degree in data science, Machine Learning Engineering, or a related technical field, or equivalent practical experience
- 5+ years of professional Data Science/ML engineering experience
- Strong applied experience building LLM-powered agents in production — shipped, multi-turn agentic systems, not just prompt experiments
- Hands-on expertise with agent orchestration frameworks — stateful graphs, tool use, and conditional routing
- Deep understanding of context engineering and tool / function-calling design for reliable agent behavior
- Practical RAG experience — embeddings, vector search, and retrieval-quality tuning
- Fluency with LLM model selection and tuning across providers, including reasoning models and their trade-offs
- Experience designing LLM evaluation — offline eval, graders, test sets, metrics, and quality gates
- Comfort with agent observability and tracing to diagnose and improve behavior
- Strong Python skills as applied to ML/agent work
- Experience with agent memory systems
- Experience with LangChain suite
- Experience building safety guardrails for high-stakes domains (clinical, financial, legal)
- Experience optimizing LLM latency, cost, and reliability at scale
- Experience with building and working with MCPs and loop engineering
- Prompt optimization techniques such as GEPA
- Working with sensitive data in regulated environment
Benefits
- All regular employees are also eligible for the corporate bonus program or a sales incentive (target included in OTE) as well as stock options.
- Competitive medical, dental, and vision coverage
- Competitive 401(k) Plan with a generous company match
- Flexible Time Off/Paid Time Off, 13 paid holidays
- Protection Plans including Life Insurance, Disability Insurance, and Supplemental Insurance
- Mental Health and Wellness benefits
Company Overview
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