Machine Learning Engineer

Summary

Transcarent is a health and care company providing medical, pharmacy, and point solutions through a generative AI-powered platform. The Machine Learning Engineer will build, tune, and evaluate production-grade multi-agent systems, including orchestration, retrieval, memory, model selection, evaluation, and safety guardrails. The role also involves collaborating with stakeholders and documenting technical designs.

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 tool s 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
• • 3+ 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
• • E xperience with building and working with MCPs and loop engineering
• • Prompt optimization techniques such as GEPA

Qualifications

Must Haves

• • Bachelor's or master's degree in data science , Machine Learning Engineering, or a related technical field, or equivalent practical experience
• • 3+ 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

Nice to Haves

• • 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
• • E xperience with building and working with MCPs and loop engineering
• • Prompt optimization techniques such as GEPA

Benefits

• All regular employees are 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