> Markdown version of [/jobs/ext/3897464-mlops-engineer-remote](https://www.wearedevelopers.com/jobs/ext/3897464-mlops-engineer-remote). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # MLops Engineer (Remote) - **Company:** Motion Recruitment Partners LLC. - **Location:** Boston, MA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computing Platforms, Cloud Computing, Encodings, Continuous Integration, Data Infrastructure, Distributed Systems, Python (Programming Language), Automation of Marketing, Performance Tuning, Software Engineering, TypeScript, Management of Software Versions, Openapi, AI Infrastructure, AWS Cdk, Data Logging, Retrieval-Augmented Generation, Large Language Models, Generative AI, Backend, Cloudformation, Discretization, Containerization, AI Platforms, Deployment Automation, Machine Learning Operations, Invoking Functions, Model Registry, Software Coding, GPT, Software Version Control, Dynatrace, GXP, Docker, Databricks - **Published:** October 23, 2026 - **Apply:** https://www.dice.com/job-detail/363aaa28-bf33-4a5b-8dbb-410454a50a63 ## About the Role 10+ years of professional experience in software engineering and infrastructure engineering designing, building, and scaling distributed cloud-native systems Demonstrated experience as a technical leader or architect responsible for system design, scalability, performance, and cost optimization Experience designing security into multi-tenant platforms, including handling sensitive data such as PII and PHI and mitigating LLM-specific risks Extensive experience building and maintaining AI/ML infrastructure in production as a multi-tenant product with external users Deep hands-on experience deploying and operating LLM-based systems including RAG architectures, retrieval and embedding design, prompt versioning, model versioning, and tool/function calling Expert-level coding skills in TypeScript and Python for backend services and APIs Production experience with model registry and serving platforms, ideally Databricks MLflow Experience building AI evaluation frameworks, regression gates, and drift or quality monitoring capabilities Experience designing scalable, secure, versioned APIs using REST and OpenAPI specifications Strong AWS and containerization experience, including Docker Experience with CloudFormation or AWS CDK and contributing to deployment automation and CI/CD processes Experience defining observability practices including monitoring, alerting, logging, distributed tracing, and SLI/SLO/SLA models Ability to influence technical direction, communicate with customers and cross-functional teams, and mentor engineers Desired Skills & Experience Familiarity with emerging LLM frameworks for advanced prompt orchestration and programmatic LLM pipelines Experience running agentic or multi-step orchestration in production Experience with MCP as an integration surface for tooling and non-human identities Understanding of LLM cost monitoring, latency optimization, and usage analytics in production environments Experience with multimodal model inputs including image and instrument data Experience with fine-tuning, distillation, quantization, batching, KV-cache strategies, or other model optimization techniques Experience delivering AI/ML systems in regulated environments including GxP, 21 CFR Part 11, or SOC 2 environments Background in scientific, life sciences, or laboratory data domains ## Description Join TetraScience, the Scientific Data and AI Cloud company, as a Lead Software Platform Engineer working at the intersection of distributed systems and MLOps. In this role, you will own and scale AI and data infrastructure that supports production-grade AI/ML workflows for scientists at some of the world's largest pharmaceutical companies. This is a highly impactful opportunity for an engineer who has built AI/ML infrastructure as a multi-tenant product rather than internal tooling. You will serve as the technical design authority for AI platforms, model serving, LLMs, agents, observability, security, and evaluation frameworks while helping define the future direction of AI infrastructure in a regulated environment.