Interested in this AI/ML Engineer role at OneMain Financial?
Apply Now →Skills & Technologies
About This Role
We are seeking an experienced and dynamic Vice President – Director responsible for AI Operations, Incident Management, Problem Management and Level 2 Application Support. This senior leader will be accountable for the strategy, execution, and continuous improvement of enterprise Incident Management, Problem Management, and Level 2 Application Support capabilities with a desired outcome of minimizing incidents and maximizing service availability.
The ideal candidate will possess a strong technology operational leadership background and a passion for building modern, proactive operations organizations that leverage automation, observability, artificial intelligence, and data-driven decision-making.
This leader will drive the transformation from reactive support models toward predictive and preventative operations through the adoption of AI Ops, intelligent automation, and continuous service improvement practices.
This role will partner closely with Engineering, Infrastructure, Architecture, Cybersecurity, Product, and Business stakeholders to improve operational resilience, reduce customer-impacting incidents, accelerate restoration times, eliminate recurring issues, and enhance the overall customer and team member experience.
Responsibilities
Develop Strategy and Vision
- Define and execute the enterprise strategy and roadmap for Incident Management, Problem Management, and Level 2 Application Support.
- Establish a long-term vision for operational excellence, resiliency, service restoration, and issue prevention.
- Develop and maintain organizational goals, key performance indicators (KPIs), and operational maturity targets.
- Ensure alignment between operational priorities, business objectives, customer experience goals and technology strategy.
Lead Incident Management
- Provide executive leadership and governance for major incident management across the enterprise.
- Establish and continuously improve incident response processes, escalation procedures, communication standards, and operational playbooks.
- Ensure rapid and effective coordination across Technology teams during critical incidents.
- Drive improvements in Mean Time to Detect (MTTD), Mean Time to Restore Service (MTTR), customer impact measurement, and incident communications.
- Partner with Observability, Monitoring, Infrastructure, and Application teams to improve detection, diagnosis, and recovery capabilities.
- Lead executive communications during significant customer or business impacting events.
Lead Problem Management and Continuous Improvement
- Own the enterprise Problem Management practice and associated governance.
- Establish rigorous root cause analysis standards and ensure corrective actions are identified, prioritized, and completed.
- Analyze operational trends, recurring incidents, and systemic risks to identify opportunities for stability improvements.
- Drive initiatives that eliminate recurring issues, reduce operational toil, and improve overall platform reliability.
- Develop reporting and executive dashboards that measure problem trends and remediation effectiveness.
- Foster a blameless culture focused on prevention rather than reaction.
Lead Level 2 Application Support
- Provide leadership for Level 2 Application Support teams responsible for diagnosing, troubleshooting, and restoring application services.
- Establish support models, staffing strategies, and operational procedures aligned to business priorities and customer outcomes.
- Ensure Standard Operating Procedures (SOPs), knowledge articles, runbooks, and troubleshooting guides remain current and effective.
- Partner closely with Level 3 Engineering teams to improve supportability, logging, instrumentation, diagnostics, and operational readiness.
- Ensure support teams are prepared to support new technologies, platforms, and application releases.
- Drive consistency and excellence across all application support functions.
Advance AI Ops and Intelligent Automation
- Develop and execute an enterprise AI Ops strategy that transforms incident detection, diagnosis, correlation, prediction, and remediation.
- Leverage machine learning, event correlation, predictive analytics, and automation technologies to identify operational issues before customer impact occurs.
- Drive adoption of intelligent alerting, automated triage, automated root cause identification, and self-healing capabilities.
- Partner with Observability, Engineering, and Data teams to build operational intelligence platforms that reduce noise, improve signal quality, and accelerate resolution.
- Identify opportunities to automate repetitive support, incident response, and problem management activities through workflows, orchestration, scripting, and AI-enabled solutions.
- Establish measurable targets for automation adoption, operational efficiency, incident reduction, and support productivity improvements.
- Continuously evaluate emerging AI, automation, and operational technologies to enhance operational resilience and effectiveness.
Collaboration and Stakeholder Engagement
- Build strong partnerships across Engineering, Infrastructure, Architecture, Cybersecurity, Product, Risk, Compliance and Operations organizations.
- Influence operational priorities and investment decisions through data-driven recommendations.
- Lead governance forums focused on operational stability, incident trends, problem remediation, and service performance.
- Present operational performance, risk assessments, and strategic recommendations to senior executives and stakeholders.
- Serve as a champion for operational excellence and customer-centric decision making throughout the organization.
Team Leadership and Organizational Development
- Lead, develop, and mentor leaders and teams responsible for Incident Management, Problem Management, and Level 2 Application Support.
- Build a high-performing organization focused on accountability, innovation, operational rigor, and continuous learning.
- Foster an environment that encourages collaboration, ownership, and professional development.
- Develop succession plans and talent strategies to ensure long-term organizational effectiveness.
- Promote a culture of continuous improvement, automation, and operational excellence.
Requirements
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field. Advanced degree preferred.
- 10+ years of progressive experience within Technology organizations supporting large-scale enterprise environments.
- 7+ years of leadership experience managing operational support, incident management, site reliability, production operations, or related functions.
- Extensive experience leading enterprise Incident Management and Problem Management programs.
- Demonstrated experience managing large-scale, customer-impacting incidents and operational recovery efforts.
- Strong understanding of application architectures, cloud platforms, infrastructure technologies, and distributed systems.
- Experience with observability, monitoring, event management, and operational intelligence platforms.
- Proven experience implementing automation, orchestration, or AI-driven operational solutions.
- Strong analytical, problem-solving, and decision-making skills.
- Excellent communication, facilitation, and executive presentation skills.
- Proven ability to influence and lead cross-functional teams in complex enterprise environments.
- Experience establishing operational metrics, KPIs, SLOs, and continuous improvement programs.
- Ability to balance strategic leadership with operational execution.
Preferred Qualifications
- Experience with AI Ops platforms
- Experience with observability and monitoring platforms such as Grafana, Elastic, OpenTelemetry, BigPanda, OpsRamp, or similar technologies.
- Experience within highly regulated industries such as financial services.
- Knowledge of cloud platforms including AWS, Azure, or Google Cloud Platform.
Who we Are
OneMain Financial (NYSE: OMF) is the leader in offering nonprime customers responsible access to credit and is dedicated to improving the financial well-being of hardworking Americans. Since 1912, we’ve looked beyond credit scores to help people get the money they need today and reach their goals for tomorrow. Our growing suite of personal loans, credit cards and other products help people borrow better and work toward a brighter future.
Driven collaborators and innovators, our team thrives on transformative digital thinking, customer-first energy and flexible work arrangements that grow lives, careers and our company. At every level, we’re committed to an inclusive culture, career development and impacting the communities where we live and work. Getting people to a better place has made us a better company for over a century. There’s never been a better time to shine with OneMain.
Because team members at their best means OneMain at our best, we provide opportunities and benefits that make their health and careers a priority. That’s why we’ve packed our comprehensive benefits package for full- and some part-timers with:
- Health and wellbeing options including medical, prescription, dental, vision, hearing, accident, hospital indemnity, and life insurances
- Up to 4% matching 401(k)
- Employee Stock Purchase Plan (10% share discount)
- Tuition reimbursement
- Paid time off (15 days’ vacation per year, plus 2 personal days, prorated based on start date)
- Paid sick leave as determined by state or local ordinance, prorated based on start date
- Paid holidays (7 days per year, based on start date)
- Paid volunteer time (3 days per year, prorated based on start date)
Target base salary range is $180K-$210K , which is based on various factors including skills and work experience. In addition to base salary, this role is eligible for a competitive compensation program that is based on individual and company performance.
OneMain Holdings, Inc. is an Equal Employment Opportunity (EEO) employer. Qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship status, color, creed, culture, disability, ethnicity, gender, gender identity or expression, genetic information or history, marital status, military status, national origin, nationality, pregnancy, race, religion, sex, sexual orientation, socioeconomic status, transgender or on any other basis protected by law.
Salary Context
This $180K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At OneMain Financial, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $180K to $210K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
OneMain Financial AI Hiring
OneMain Financial has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Baltimore, MD, US. Compensation range: $210K - $210K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.