Senior Engineer, Machine Learning/AI Ops

Accountabilities

• Design, build, deploy, and operate AWS infrastructure and services supporting machine learning models, internal AI tools, and event-driven integrations.

• Develop and maintain event-driven integrations and change data capture (CDC) pipelines between internal systems and third-party platforms, including Salesforce and Braze.

• Design reliable and recoverable integration workflows that account for idempotency, event replay, schema evolution, out-of-order events, and failure handling.

• Build, deploy, and maintain containerized inference APIs and backend services with secure, versioned interfaces and production-grade performance.

• Manage infrastructure as code using Terraform across multiple AWS accounts and environments, leveraging reusable modules and CI-gated workflows.

• Develop and maintain machine learning workflows for training, retraining, scoring, batch processing, and real-time inference.

• Partner with Data Science teams to establish repeatable processes for moving models from experimentation into production.

• Implement observability across machine learning platforms, AI tools, and integration services, including logging, metrics, alerting, and model monitoring.

• Support the secure operation of internal large language model (LLM) and agentic systems, including solutions built with Amazon Bedrock, through appropriate access controls, auditing, and data protection measures.

• Collaborate with Data Engineering teams on Snowflake datasets, data access, and governance requirements supporting machine learning and operational workloads.

• Operate and maintain PostgreSQL-backed production services, ensuring reliable schema migrations, effective indexing, and data correctness under concurrent writes.

• Lead incident response activities, participate in on-call rotations, and develop runbooks and reusable engineering patterns that improve operational readiness.

• Establish and promote platform engineering standards, safe deployment practices, and continuous improvement across production systems.

• Work cross-functionally to identify technical risks, resolve integration challenges, and maintain reliable, secure, and scalable AI infrastructure.

Requirements

• At least six years of experience in MLOps, platform engineering, DevOps, site reliability engineering (SRE), backend engineering, or a related discipline, including substantial experience supporting production machine learning, data, or event-driven workloads.

• A bachelor's degree in a relevant technical field.

• Deep, hands-on experience designing and operating production workloads on AWS, including compute, messaging, infrastructure, and security services.

• Strong professional Python programming skills, with experience building and operating production APIs, backend services, and integrations.

• Demonstrated experience building event-driven integrations with third-party APIs, including change data capture pipelines.

• Strong Terraform and infrastructure-as-code expertise across multiple production environments.

• Practical experience deploying and operating machine learning models, including batch and real-time serving, model versioning, and monitoring.

• Experience with containerization, CI/CD pipelines, and safe, reliable production deployment practices.

• Hands-on experience operating PostgreSQL in production, including schema migrations, indexing strategies, and maintaining data integrity under concurrent writes.

• Strong understanding of production system reliability, security, observability, troubleshooting, and operational support.

• The ability to work independently, take ownership of outcomes, and make sound technical decisions without relying on prescriptive instructions.

• Excellent communication and collaboration skills, with the ability to influence engineering standards and work effectively across technical disciplines.

• Strong organizational and analytical skills, including the ability to manage multiple projects, reprioritize quickly, and deliver high-quality results in a fast-paced environment.

• Preferred: Experience operating or securing internal LLM-based applications and agentic systems.

• Preferred: Familiarity with machine learning observability, experiment tracking, or model drift detection tools.

• Preferred: Hands-on experience with Snowflake supporting machine learning or production data workloads.

• Preferred: Experience in financial services, fintech, or another regulated industry.

• Availability for a regular full-time position, with the ability to participate in on-call support as required.

Benefits

• Annual salary: $150,500–$173,000 USD, with individual compensation determined by location, relevant skills, experience, education, and training.

• Work arrangement: Remote position within the United States.

• Healthcare coverage: Medical, dental, and vision benefits.

• Retirement savings: 401(k) plan with employer matching.

• Paid leave: Paid holidays, vacation, sick days, and volunteer time off.

• Parental leave: 12 weeks of paid parental leave.

• Insurance: Life insurance at no cost to employees, along with additional voluntary benefits.

• Transportation: Pre-tax transit benefits.

• Pet care: Discounted ASPCA pet health insurance.

• Wellness: Employee wellness incentive program.

• Career development: Mentorship and leadership programs, with opportunities for professional growth.

• Work environment: A collaborative, team-oriented culture focused on employee development and continuous improvement.