Sr AI/ML Ops Engineer
About The Position
This role designs and delivers scalable operational capabilities for complex production artificial intelligence and machine learning solutions, including models, pipelines, agents, and shared platform services. It applies advanced technical judgment to translate ambiguous reliability, deployment, observability, security, governance, and lifecycle needs into durable engineering solutions. The position leads defined technical workstreams, diagnoses systemic production issues, establishes service-level and operational readiness practices, and builds automation that strengthens release quality, resiliency, recovery, capacity management, and cost control. The engineer partners with AI/ML engineering, platform, data, architecture, security, risk, and governance stakeholders to influence solution direction, review technical approaches, and guide teams in adopting consistent AI/ML operations practices. The role also implements LLMOps and agentic operations capabilities and supports model risk management requirements in production AI/ML pipelines.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Data Science, or a related discipline, or four years of equivalent work experience in lieu of a bachelor's degree.
- At least four years of experience in AI/ML operations, ML engineering, platform engineering, cloud infrastructure, DevOps, site reliability engineering, or a related field directly aligned to the position's responsibilities.
Responsibilities
- Design scalable operational patterns for AI/ML models, pipelines, agents, and supporting services across complex environments.
- Develop observability for availability, latency, performance, drift, quality, safety, capacity, and cost.
- Engineer CI/CD, versioning, release governance, progressive deployment, rollback, and recovery capabilities.
- Diagnose complex incidents and systemic reliability issues, lead root-cause analysis, and drive preventive action.
- Define service-level indicators, operational readiness criteria, runbooks, dashboards, and lifecycle controls.
- Build automation for testing, validation, monitoring, incident response, capacity management, and lifecycle operations.
- Translate ambiguous requirements into technical designs and measurable operational outcomes.
- Lead technical workstreams, review solution approaches and code, and guide less experienced colleagues.
- Maintain knowledge of current and emerging developments and trends for assigned areas of responsibility.
- Support organizational and departmental initiatives and incorporate applicable standards into current and future solutions.
- Identify and recommend process improvements that reduce workloads, improve quality, or strengthen operational outcomes.
- Implement and maintain LLMOps and agentic operations capabilities, including prompt versioning, agent orchestration observability, tool-use auditing, guardrail enforcement, and output safety controls for production LLM-based and agentic systems.
- Support model risk management requirements by implementing validation gates, challenger model workflows, audit trail controls, and compliance checkpoints in production AI/ML pipelines consistent with applicable financial-services regulatory standards.
- Serve as a technical resource to team members and applicable internal and external stakeholders.
Benefits
- PTO and parental leave
- Competitive 401(k) and employee benefits
- Free financial counseling, health coaching, and employee assistance program
- Tuition assistance program
- Leadership development and virtual training opportunities
- Work arrangements that work for you
- Productivity and technology tools and training
- The role may be eligible for the discretionary Annual Incentive Program; other rewards may include long-term incentives and sales incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
