Senior MLOps Engineer

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Clutch

WebsiteLinkedInAll Job Openings

51 - 200 employees

☁️ SaaS

🏢 Enterprise

Consulting • SaaS • Enterprise

Clutch is a results-driven consulting firm that redefines traditional consulting methods through a human-centered approach. By focusing on data-driven solutions, Clutch addresses gaps in business processes for companies of all sizes, including government entities, small businesses, and financial institutions. It specializes in marketing, communications, and event management, and utilizes proprietary tools and resources to drive transformational outcomes. Notably, Clutch applies a Human Transformation Approach to re-integrate human elements often overlooked by technology-led solutions. It offers AI-driven insights to predict and quantify human emotions, enhancing understanding and support for entrepreneurs and the startup marketplace.

📋 Description

• 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 traffic • Build the first repeatable deployment pipeline: model artifact → versioned, deployable, rollback-able production service, with infrastructure defined as code • Stand up the monitoring foundation: latency/error/drift dashboards, alerting, and audit/trace visibility across models and agents • Build a working relationship with HAL and become the data team's go-to on ML serving and reliability decisions • Be the primary owner (with data engineer support) of the ML serving platform and deployment pipelines for NBA and our ML models • Have at least one production model and one production agent fully instrumented — versioning, monitoring, alerting, and multi-tenant gating in place • Define the data team's playbook for shipping a new ML model to production, end-to-end • Drive architectural decisions across APIs, processing pipelines, distributed compute, storage, search, observability, cloud infrastructure, and model-serving workflows • Mentor the data engineers on MLOps patterns so they can confidently support and extend the systems you own • 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 reliably • Have measurably improved cost and latency • Be shaping the data team's roadmap for the next generation of ML infrastructure, in partnership with the PM and data scientist • Help us decide what to hire next as the team scales

🎯 Requirements

• 8+ 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 deploy • Strong 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 enough • CI/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 and reproducible • Monitoring & observability discipline. You instrument serving systems for latency, error rates, drift, and cost; you read audit rows and distributed traces; you set up alerting so regressions are caught before users feel them. You treat monitoring as a first-class deliverable, not an afterthought • Reliability rigor. You design for failure: structured error handling, graceful degradation, rollback paths, and runbooks. You have a story about a production incident you handled and how you hardened the system afterward • Experience building and operating low-latency production APIs (FastAPI, BentoML, or equivalent), with opinions on serving, batching, and caching • Comfortable in AWS (Lambda especially), containers (Docker), and GitHub-based workflows • Security & governance. You ensure security and governance across systems: IAM, KMS, access policies, and Secrets Manager/SSM • DevOps / infrastructure knowledge, plus data manipulation and feature engineering • Solid understanding of ML concepts: models, pipelines, metrics, and supervised/unsupervised learning • Integrate and optimize AI/ML services with the company's other systems • You use AI tooling actively in your engineering workflow — not as a novelty, but as a default. You'll be expected to demonstrate this during the technical evaluation • Databricks, PySpark

🏖️ Benefits

• Remote Flexibility: Enjoy the freedom of remote work from anywhere, balancing life and career seamlessly. • Unforgettable Off-Sites: Twice a year, bond with colleagues in exciting destinations, fostering teamwork and fresh ideas. • Paid Time Off and National Holidays: Enjoy 20 PTO days yearly and the National Holidays for relaxation and rejuvenation. • Stock Options: Joining us means having a stake in our success, so you'll receive stock options as part of your compensation package. • Home Office Setup: Create your ideal workspace with a dedicated budget for home office essentials. • Work Trip Budget: Grow personally and professionally with a budget for work-related trips and co-working.