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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Transcarent, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $145,000.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** Python (Programming Language), Machine Learning, Search Technologies, Large Language Models, Multi-Agent Systems, Optimization Algorithms - **Published:** July 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=024c420b3e336881 ## About the Role * 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. Nice to have * 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. ## Description As a Senior ML Engineer, you build production grade multi agentic systemsthat guide people through complex, high-stakes conversations. Our systems combine multi-step agent orchestration, retrieval, memory, and rigorous evaluation and safety layers. We're looking for a Senior ML Engineer to design, build, tune, and evaluate these agentic systems end to end - from context engineering and tool design, through retrieval and memory, to evaluation and safety guardrails. This is an applied-ML and LLM-systems role focused on agent behavior, model selection, retrieval of quality, and evaluation. Though understanding of AI/ML is crucial for this role, we kindly request that you refrain from using GenAI while going through the interview process to allow fair evaluation of your skillset. What you'll do * 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. ## Related Videos - [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Building an agentic software factory: How we rebuilt product development at Pipedrive](https://www.wearedevelopers.com/videos/100261-building-an-agentic-software-factory-how-we-rebuilt-product-development-at-pipedrive) - [Designing and Deploying Distributed Multimodal Multi-Agent Systems with Google's AI Stac](https://www.wearedevelopers.com/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac) - [Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation](https://www.wearedevelopers.com/videos/1235-carl-lapierre-exploring-advanced-patterns-in-retrieval-augmented-generation) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Introducing Redis Agent Memory Server](https://www.wearedevelopers.com/magazine/699-introducing-redis-agent-memory-server) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)