ML Ops Engineer
- Compensation
- Not disclosed
- Location
- New York City, United States
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Devops
- Posted
Skills
Must have
- 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
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
Full description
Confido is the AI infrastructure powering modern CPG — the platform that 200+ brands like OLIPOP, Simple Mills, Dr. Squatch, and Tropicana use to run everything from deductions to production planning. Finance, accounting, sales, and operations, unified in one system for the first time.
We're growing 5x year over year with a small team in New York City; the people who join now will shape the product, the culture, and the company itself.
If you want your work on shelves everywhere — and outsized ownership while you build — we'd love to meet you.
The Role
Be the first dedicated owner of Confido's ML platform. Our AI/ML team already ships document-understanding, forecasting, and agentic systems into production — on infrastructure we've stood up by hand. You'll own that layer: the pipelines, serving, and cloud foundation that turn models and agents into reliable, cost-efficient production systems, at the scale of hundreds of thousands of documents and heavy LLM/VLM workloads.
Location: New York, NY (Relocation supported)
What you'll do
-
Own ML pipelines end to end — experimentation to production — and the infrastructure behind training, inference, and agentic workloads
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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
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Stand up the cloud foundation as Infrastructure as Code and the CI/CD that ships ML safely
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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)
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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
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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
What we're looking for
Required
-
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