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.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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