Senior ML Ops Engineer

What you'll do

• Own ML pipelines end to end — experimentation to production — and the infrastructure behind training, inference, and agentic workloads
• Give the AI/ML team a paved road: reproducible environments and fast paths from prototype to production, so they can try new models and agents without fighting the infra
• Stand up the cloud foundation as Infrastructure as Code and the CI/CD that ships ML safely
• Serve and optimize inference and forecasting workloads — latency, throughput, and cost — and the data streams feeding them (e.g. turning a heavy synchronous model call into an async, parallelized one)
• Own the data interface with data engineering: serve the right data to models and agents, and write their outputs back into the platform's data systems for the rest of Confido to use
• Make reliability, observability, security, and privacy the default — and keep model and agent quality measurable in production through online evals and human-in-the-loop review, not just uptime

Requirements

• 5+ years in MLOps, ML platform, AI infrastructure, or platform engineering — on production ML systems, not pipelines on paper
• Live at the seam of software and infrastructure: equally at home writing production code and standing up cloud infra
• Driven a real pipeline end to end and can walk through it: the architecture, the security and cost trade-offs, and what you'd change
• Deep cloud infrastructure understanding, distributed data systems, and IaC — you can boot an environment from scratch, wire CI/CD, and run containerized workloads in production without hand-holding
• Strong Python and comfort in a production app codebase (Ruby, Java)
• Monitoring, security, and cost are instincts, not afterthoughts
• High ownership in a fast-moving startup, and experience productionizing what research/AI teams build

Nice to have

• LLMOps tooling — tracing, prompt/version management, eval harnesses
• Inference optimization (vLLM, ONNX, TensorRT) and GPU / spot-instance economics
• ML platform and orchestration tooling (MLflow, BentoML, Ray, Airflow)
• Large-scale data systems (Snowflake, Kafka) and vector databases
• Managed ML services (Bedrock, SageMaker, Vertex AI)
• Multimodal or generative AI in production

Stack

Python · Ruby/Rails · AWS · Terraform · Kubernetes · GitHub Actions · Snowflake · Aurora/RDS · Redis · Kafka

Perks + Benefits

• Equity — own a piece of what you're building
• Fully paid health coverage with Aetna (we cover 100% of premiums)
• Top-tier dental and vision through Guardian
• 12 weeks paid parental leave
• Unlimited PTO, plus regular 4-day holiday weekends we actually take
• 401(k) through Vestwell
• Paid relocation — we'll get you here
• Full desk setup on day one (laptop, monitor, keyboard) + a $200 stipend to make it yours
• Catered Friday lunches, team dinners on us, and unlimited coffee + snacks featuring our own brands