Senior MLOps Engineer

Senior MLOps Engineer LocationBrazilEmployment TypeContractLocation TypeRemoteDepartmentEngineeringAbout the RoleWe're hiring a Senior MLOps Engineer to be the data team's owner of production ML operations. You'll build the pipelines that take models from prototype to production, own the low-latency serving API behind our Next Best Action (NBA) engine, and stand up the monitoring, alerting, and reliability layer that keeps NBA models — and the LLM agents that consume them — healthy in production. This is a builder's role at a builder's moment: NBA is going live, the production ML platform is being shaped now, and you'll define how Clutch ships and operates AI for years to come. When there isn't active MLOps work, you'll also contribute to data engineering and machine learning work across the team.About the TeamThe Data team today is five people: one data scientist, two data engineers, one data analyst, and one product manager. We're small, ambitious, and shipping fast — ML models heading to production, a serving API being built, and AI agents in active development. You'll be the senior MLOps voice inside the team and the operational bridge to HAL, the platform team that runs Clutch's agent runtime. Expect tight feedback loops, real autonomy, and a team that values pragmatism over purity.What You'll DoWithin 3 months, you will:Take ownership of the ML serving API that serves NBA recommendations, partnering with the data engineer who's been building it, and harden it for low-latency production trafficBuild the first repeatable deployment pipeline: model artifact → versioned, deployable, rollback-able production service, with infrastructure defined as codeStand up the monitoring foundation: latency/error/drift dashboards, alerting, and audit/trace visibility across models and agentsBuild a working relationship with HAL and become the data team's go-to on ML serving and reliability decisionsWithin 6 months, you will:Be the primary owner (with data engineer support) of the ML serving platform and deployment pipelines for NBA and our ML modelsHave at least one production model and one production agent fully instrumented — versioning, monitoring, alerting, and multi-tenant gating in placeDefine the data team's playbook for shipping a new ML model to production, end-to-endDrive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model-serving workflowsMentor the data engineers on MLOps patterns so they can confidently support and extend the systems you ownWithin 9 months, you will:Operate as the technical lead within the data team for NBA production ML operations — the person other teams come to when they want to understand how Clutch ships and runs ML reliablyHave measurably improved cost and latencyBe shaping the data team's roadmap for the next generation of ML infrastructure, in partnership with the PM and data scientistHelp us decide what to hire next as the team scalesWhat You'll BringRequired8+ years of experience in software, data, or ML engineering, with 4–5+ years running ML systems in production — you've taken models from prototype to production and own what happens after deployStrong Python — most of the work (serving API, pipelines, tooling, data pipelines) is in Python, and you're comfortable in production codebases, not just notebooks. Some TypeScript is involved for integration with our agent runtime — you don't need to be an expert, comfort with a second language is enoughCI/CD & deployment discipline. You build training and deploy pipelines that take a model artifact to a versioned, deployable, rollback-able production service, with automated testing and reproducible builds. You've implemented CI/CD for ML and built and maintained CI/CD pipelines (GitHub Actions, Bamboo, GitLab CI, or similar)Infrastructure as code. You manage cloud infrastructure (AWS Lambda, ECS) with Terraform or equivalent — no click-ops, everything reviewable...