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About This Role
Date: Sep 25, 2026
Primary Location: Radnor, PA, US
Company: Lincoln Financial
Alternate Locations: Radnor, PA (Pennsylvania); Charlotte, NC (North Carolina); Fort Wayne, IN (Indiana); Greensboro, NC (North Carolina)
Work Arrangement:
Hybrid : Employee will work 3 days a week in a Lincoln office
Relocation assistance: is not available for this opportunity.
Requisition #: 76681
The Role at a Glance
We are creating a new AI/ML Ops pillar to own the reliability, safety, and cost-effectiveness of every model, pipeline, and agent we run in production. This is fundamentally an operations leadership role: someone who has built monitoring, escalation, and change management discipline from the ground up during a technology or business transformation, and who can apply that same rigor to AI/ML systems. This leader will build and run the function that closes the gap between "built" and "running well at scale." They will report to the Chief Data & AI Engineering Officer and sit alongside the leaders of AI/Data Infrastructure, Data Strategy & Governance, Data & Delivery Platform, AI/ML/Agentic Engineering, and AI Native Intelligence Platform.
What you'll be doing
Operating Foundation & Change Management
- Define the operating model for how AI/ML systems move from build to production: intake, change approval, release governance.
- Build change management processes for model, pipeline, and agent releases, including change advisory review, risk tiering, rollback authority, and communication protocols.
- Establish a clear ownership and accountability model (RACI) across Engineering, Ops, and Governance for every production AI/ML asset.
Monitoring, Observability & Notifications
- Build monitoring for performance, drift, latency, quality, and safety across production AI/ML systems.
- Design the notification and alerting architecture: what gets flagged, to whom, at what severity, with what SLA for acknowledgment.
- Implement in-production evals and guardrails; enforce governance policy at runtime in partnership with Data Strategy & Governance.
Incident Response & Escalation Management
- Define and own SLOs/SLAs for production models, pipelines, and agents.
- Build on-call rotations, incident response, escalation tiers, and postmortem practice for AI/ML systems.
- Own the detection and escalation path for misuse, abuse, or safety incidents.
- Drive mean-time-to-detect and mean-time-to-recovery down as the estate scales.
Deployment & Lifecycle Governance
- Partner with Platform Engineering to stand up CI/CD, versioning, and rollback for models, pipelines, and agents, partnering with Engineering on execution.
- Establish safe rollout patterns (canary, shadow, staged) in partnership with Engineering.
- Own the retirement and deprecation process for models and agents no longer in use.
Cost & Capacity
- Own inference and compute cost visibility and optimization; build unit economics per model and agent.
- Partner with AI/Data Infrastructure on capacity planning. Organization Building
- Define the operating model, team structure, and hiring plan for the pillar.
- Build the feedback loop that routes production failure modes and cost signals back to Engineering.
- Establish the pillar's KPIs and report on them to the Chief Data & AI Engineering Officer and broader leadership.
What we’re looking for
- Proven track record leading operations through a major transformation: building the operating model, monitoring and observability foundation, and escalation discipline for a function that didn't have one before, ideally in technology, platform, or shared-services operations.
- Deep experience in change management: standing up change advisory processes, risk tiering, release governance, and rollback authority in a fast-moving technical environment.
- Strong background in monitoring, observability, and notification and escalation design: knowing what to alert on, who gets paged, and how severity and ownership are defined.
- Experience building incident response and postmortem practices, including on-call rotations and MTTD/MTTR reduction, in any production technology environment. AI/ML-specific experience is a strong plus, not a prerequisite.
- Working knowledge of (or demonstrated ability to quickly learn) ML/LLM system failure modes: drift, degradation, hallucination, latency and cost blowups, and how to instrument against them.
- Experience partnering with governance and risk functions to enforce policy in live systems.
- Strong track record hiring, developing, and leading technical or operational teams, ideally from an early stage.
- Comfortable operating with ambiguity and building process from scratch, not just running an existing playbook.
Application Deadline
What’s it like to work here?
At Lincoln Financial, we love what we do. We make meaningful contributions each and every day to empower our customers to take charge of their lives. Working alongside dedicated and talented colleagues, we build fulfilling careers and stronger communities through a company that values our unique perspectives, insights and contributions and invests in programs that empower each of us to take charge of our own future.
What’s in it for you:
- Clearly defined career tracks and job levels, along with associated behaviors for each of Lincoln's core values and leadership attributes
- Leadership development and virtual training opportunities
- PTO/parental leave
- Competitive 401K and employee benefits
- Free financial counseling, health coaching and employee assistance program
- Tuition assistance program
- Work arrangements that work for you
- Effective productivity/technology tools and training
The pay range for this position is with anticipated pay for new hires between the minimum and midpoint of the range and could vary above and below the listed range as permitted by applicable law. Pay is based on non-discriminatory factors including but not limited to work experience, education, location, licensure requirements, proficiency and qualifications required for the role. The base pay is just one component of Lincoln’s total rewards package for employees. In addition, the role may be eligible for the Annual Incentive Program, which is discretionary and based on the performance of the company, business unit and individual. Other rewards may include long-term incentives, sales incentives and Lincoln’s standard benefits package.
About The Company
Lincoln Financial (NYSE: LNC) helps people to confidently plan for their version of a successful future. We focus on identifying a clear path to financial security, with products including annuities, life insurance, group protection, and retirement plan services.
With our 120-year track record of expertise and integrity, millions of customers trust our solutions and service to help put their goals in reach.
Lincoln Financial Distributors, a broker-dealer, is the wholesale distribution organization of Lincoln Financial. Lincoln Financial is the marketing name for Lincoln Financial Corporation and its affiliates including The Lincoln National Life Insurance Company, Fort Wayne, IN, and Lincoln Life & Annuity Company of New York, Syracuse, NY. Lincoln Financial affiliates, their distributors, and their respective employees, representatives and/or insurance agents do not provide tax, accounting or legal advice.
Lincoln is committed to creating an inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
Follow us on Facebook, X, LinkedIn, Instagram, and YouTube. For the latest company news, visit our newsroom.
Be Aware of Fraudulent Recruiting Activities
If you are interested in a career at Lincoln, we encourage you to review our current openings and apply on our website. Lincoln values the privacy and security of every applicant and urges all applicants to diligently protect their sensitive personal information from scams targeting job seekers. These scams can take many forms including fake employment applications, bogus interviews and falsified offer letters.
Lincoln will not ask applicants to provide their social security numbers, date of birth, bank account information or other sensitive information in job applications. Additionally, our recruiters do not communicate with applicants through free e-mail accounts (Gmail, Yahoo, Hotmail) or conduct interviews utilizing video chat rooms. We will never ask applicants to provide payment during the hiring process or extend an offer without conducting a phone, live video or in-person interview. Please contact Lincoln's fraud team at [email protected] if you encounter a recruiter or see a job opportunity that seems suspicious.
Additional Information
This position may be subject to Lincoln’s Political Contribution Policy. An offer of employment may be contingent upon disclosing to Lincoln the details of certain political contributions. Lincoln may decline to extend an offer or terminate employment for this role if it determines political contributions made could have an adverse impact on Lincoln’s current or future business interests, misrepresentations were made, or for failure to fully disclose applicable political contributions and or fundraising activities.
Any unsolicited resumes or candidate profiles submitted through our web site or to personal e-mail accounts of employees of Lincoln Financial are considered property of Lincoln Financial and are not subject to payment of agency fees.
Lincoln Financial ("Lincoln" or "the Company") is an Equal Opportunity employer and, as such, is committed in policy and practice to recruit, hire, compensate, train and promote, in all job classifications, without regard to race, color, religion, sex, age, national origin or disability. Opportunities throughout Lincoln are available to employees and applicants are evaluated on the basis of job qualifications. If you are a person with a disability that impedes your ability to express your interest for a position through our online application process, or require TTY/TDD assistance, contact us by calling 260-455-2558.
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 9,653 AI roles we're tracking, MLOps Engineer positions make up 2% of the market. At Lincoln Financial, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $205,000 based on 151 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Lincoln Financial AI Hiring
Lincoln Financial has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, MLOps Engineer. Positions span Radnor, PA, US, Radnor, PA. Compensation range: $171K - $274K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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