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About This Role
Description
===============
Summary:
The MLOps Automation Engineering Senior Lead will lead a team responsible for building and deploying MLOps Automation for some of Huntington’s most valuable and most challenging data\-driven projects.
Duties and Responsibilities:
- Streamline the data, analytics, and model development lifecycle by identifying pain points and productivity barriers and determining ways to resolve them through automation.
- Helps set the strategy and tone for MLOps Automating Engineering strategy and vision for the future.
- Understand the current process and technical complexities of developing and deploying data pipelines and model builds and develop automation solutions to improve and extend the existing process to become an unattended delivery pipeline.
- Collaborate closely with product development, architecture, data engineering and testing teams to understand their current build and release processes and make recommendations for improvement through the automation of various tasks.
- Partner with cross\-functional stakeholders, including development, operations, quality assurance and security, to streamline processes.
- Develop and continuously improve automation solutions to enable teams to build and deploy quality data and code efficiently and consistently.
- Build automated testing solutions in support of quality management objectives to reduce manual effort.
- Build automated environment provisioning solutions in response to changes in processing demand.
- Build automated feedback mechanisms to monitor the performance of models in production.
- Work closely with cross\-functional stakeholders to analyze and troubleshoot complex production issues.
- Prepare and present design and implementation documentation to multiple stakeholders.
- Promote automation across the data management and analytics delivery organization.
- Perform other duties as assigned.
Basic Qualifications:
- Bachelor’s Degree (Computer Science, Business Administration, Economics or related fields) or equivalent relevant work experience
- 10\+ years of relevant automation engineering experience, of software engineering, in strategy, management consulting, or similar skillset, and of technical leadership experience with data\-centric products
- 10\+ years of experience with one or more coding languages (e.g., JavaScript, C\+\+, Python, Java), CI/CD tools (e.g., Jenkins, Artifactory, CircleCI, Ansible), and development platforms (e.g., AWS, Azure, Docker, Kubernetes)
Preferred Qualifications:
- Strong collaboration skills, with a demonstrated ability to work well as part of a team
- Experience developing CI/CD workflows and tools
- Strong automation scripting skills
- Experience in configuration management, test\-driven development, and release management.
- Strong analytical and troubleshooting skills.
- Experience with agile development and strong understanding of DataOps and ModelOps principles
- Ability to investigate and analyze information, and to draw conclusions
- Flexibility, adaptability, and desire to learn new languages and technologies
- Strong verbal and written communication skills
- Demonstrated ability to work independently across multiple tasks while meeting aggressive timelines
- Strategic, intellectually curious thinker with focus on outcomes
- Professional image with the ability to form relationships across functions
- Ability to train more junior analysts regarding day\-to\-day activities, as necessary
- Proven ability to lead cross\-functional efforts
- Willingness and ability to learn new technologies on the job
Financial Services background
*
Exempt Status: (Yes \= not eligible for overtime pay) ( No \= eligible for overtime pay)
Yes
Workplace Type:
Office
Our Approach to Office Workplace Type
Certain positions outside our branch network may be eligible for a flexible work arrangement. We’re combining the best of both worlds: in\-office and work from home. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. Remote roles will also have the opportunity to come together in our offices for moments that matter. Specific work arrangements will be provided by the hiring team.
Compensation Range:
Total Base Pay Range 93,000\.00 \- 189,000\.00 USD Annual
The compensation range represents the anticipated low and high end of the base compensation range for this position. Actual compensation will vary based on various factors including but not limited to location, experience, and education. Colleagues in this position are also eligible to participate in an applicable incentive compensation plan. In addition, Huntington provides a variety of benefits to colleagues, including health insurance coverage, wellness program, life and disability insurance, retirement savings plan, paid leave programs, paid holidays and paid time off (PTO).
Huntington is an Equal Opportunity Employer.
Note to Agency Recruiters: Huntington will not pay a fee for any placement resulting from the receipt of an unsolicited resume. All unsolicited resumes sent to any Huntington colleagues, directly or indirectly, will be considered Huntington property. Recruiting agencies must have a valid, written and fully executed Master Service Agreement and Statement of Work for consideration.
Salary Context
This $93K-$189K range is in the lower quartile for MLOps Engineer roles in our dataset (median: $190K across 22 roles with salary data).
View full MLOps Engineer salary data →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 3,823 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Huntington Bank, 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 $217,200 based on 87 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($141K) sits 35% below the category median. Disclosed range: $93K to $189K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Huntington Bank AI Hiring
Huntington Bank has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Columbus, OH, US. Compensation range: $189K - $189K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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