Senior ML Ops Engineer

Confido•New York, NY

2d•$210,000 - $300,000•Onsite

About The Position

Confido is seeking a Senior ML Ops Engineer to be the first dedicated owner of their ML platform. The AI/ML team at Confido is already deploying document-understanding, forecasting, and agentic systems into production. This role will be responsible for the infrastructure, pipelines, and serving layer that ensures these models and agents operate as reliable, cost-efficient production systems, capable of handling large volumes of documents and significant LLM/VLM workloads. Confido is a rapidly growing company, having recently raised $15M in Series A funding. The team is based in New York City and offers opportunities for significant ownership and impact.

Requirements

5+ years in MLOps, ML platform, AI infrastructure, or platform engineering, with experience on production ML systems.
Proficiency at the intersection of software and infrastructure, comfortable writing production code and managing cloud infrastructure.
Proven experience driving a real ML pipeline end to end, including architecture, security, and cost trade-offs.
Deep understanding of cloud infrastructure, distributed data systems, and IaC; ability to set up environments, configure CI/CD, and run containerized workloads independently.
Strong Python skills and familiarity with production application codebases (Ruby, Java).
Instinctive focus on monitoring, security, and cost.
High sense of ownership in a fast-moving startup environment.
Experience productionizing work from research/AI teams.

Nice To Haves

Experience with LLMOps tooling (tracing, prompt/version management, eval harnesses).
Expertise in inference optimization (vLLM, ONNX, TensorRT) and GPU/spot-instance economics.
Familiarity with ML platform and orchestration tooling (MLflow, BentoML, Ray, Airflow).
Experience with large-scale data systems (Snowflake, Kafka) and vector databases.
Experience with managed ML services (Bedrock, SageMaker, Vertex AI).
Experience with multimodal or generative AI in production.

Responsibilities

Own ML pipelines end to end — experimentation to production — and the infrastructure behind training, inference, and agentic workloads.
Provide a streamlined experience for the AI/ML team with reproducible environments and efficient paths from prototype to production.
Establish the cloud foundation using Infrastructure as Code and implement CI/CD for safe ML deployment.
Optimize inference and forecasting workloads for latency, throughput, and cost, including data stream management.
Manage the data interface with data engineering, ensuring models and agents receive the correct data and their outputs are integrated back into the platform.
Ensure reliability, observability, security, and privacy by default, and maintain measurability of model and agent quality in production through online evaluations and human-in-the-loop review.