Lead AI/ML Ops Engineer

Lincoln Financial•Radnor, PA

2d•$152,250 - $243,600•Hybrid

About The Position

The Lead AI/ML Ops Engineer provides technical leadership for the strategy, design, delivery, and ongoing evolution of AI/ML operations capabilities across complex production environments. The role develops scalable engineering patterns, automation, controls, and operating standards so models, pipelines, agents, and shared AI services can be deployed and managed reliably, securely, responsibly, and cost effectively. The position directs complex technical initiatives, evaluates architecture and technology options, resolves cross-team dependencies, and balances speed, resiliency, security, governance, performance, and cost. It works across AI/ML engineering, platform, data, architecture, security, risk, governance, and business teams to influence enterprise practices, provide technical leadership during critical production events, mentor engineers, and improve operational maturity and sustainable AI/ML adoption. The position is hybrid, with the employee working three days a week in a Lincoln office. Alternate locations are Radnor, Pennsylvania; Charlotte, North Carolina; Fort Wayne, Indiana; and Greensboro, North Carolina. Relocation assistance is not available. The listed pay range is $152,250–$243,600, with anticipated pay for new hires between the minimum and midpoint. The role may be eligible for a discretionary annual incentive and other rewards, including long-term incentives, sales incentives, and Lincoln’s standard benefits package.

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 five 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

Lead end-to-end design and delivery of operational capabilities across multiple teams or solution areas.
Establish observability, service-level, capacity, performance, safety, and cost-management standards.
Define scalable patterns for CI/CD, versioning, release governance, progressive deployment, rollback, disaster recovery, and lifecycle management.
Provide technical leadership during critical incidents and drive systemic corrective and preventive improvements.
Lead development of reusable automation, tooling, dashboards, runbooks, readiness controls, and engineering standards.
Evaluate architecture and technology options, balancing reliability, security, governance, speed, and cost tradeoffs.
Partner with engineering, platform, data, architecture, security, risk, and governance leaders to shape production standards.
Lead complex technical initiatives, review designs and code, mentor engineers, and influence adoption across teams.
Maintain knowledge of current and emerging developments and trends in 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.
Lead 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.
Operationalize model risk management requirements by embedding validation gates, challenger model workflows, audit trail controls, and compliance checkpoints into production AI/ML pipelines in alignment with financial-services regulatory standards.
Serve as a technical resource to team members and applicable internal and external stakeholders.