Senior ML Ops Engineer

About the role

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

Responsibilities

• 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
You live at the seam of software and infrastructure: equally at home writing production code and standing up cloud infra. You've 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

Qualifications

• What we're looking for

Skills

• Python
• Ruby/Rails
• AWS
• Terraform
• Kubernetes
• GitHub Actions
• Snowflake
• Aurora/RDS
• Redis
• Kafka
• Large-scale data systems (Snowflake, Kafka)
• Vector databases
• Managed ML services (Bedrock, SageMaker, Vertex AI)
• Multimodal or generative AI in production

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

Pay

$210K - $300K

Schedule

Full-time