Senior ML Ops Engineer
Be the first dedicated owner of Confido's ML platform, owning end-to-end ML pipelines, infrastructure for training/inference/agentic workloads, and providing reproducible environments for the AI/ML team in a fast-growing CPG AI startup.
About the job
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
Skills
MLOps, Python, AWS, Terraform, Kubernetes, MLflow, Bentoml, Ray, Airflow, Snowflake, Kafka, vLLM, Onnx, TensorRT, SageMaker
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