Interested in this MLOps Engineer role at HCA Healthcare?
Apply Now →About This Role
Experience the HCA Healthcare difference where colleagues are trusted, valued members of our healthcare team. Grow your career with an organization committed to delivering respectful, compassionate care, and where the unique and intrinsic worth of each individual is recognized. Submit your application for the opportunity below: Senior Staff MLOps Engineer
Job Summary and Qualifications
Position SummaryThe Senior Staff ML Ops Engineer is a technical leader and key architect in shaping and driving our organization's MLOps strategy and practice. This role requires deep expertise in cloud architecture, machine learning operations, and DevOps principles, combined with a proven ability to lead technical initiatives, mentor engineers, and collaborate effectively with cross-functional teams.The Senior Staff ML Ops Engineer will be responsible for designing, building, and maintaining a highly scalable, reliable, and secure MLOps platform that enables the efficient deployment and operation of AI/ML solutions across the organization. This role will champion best practices, drive innovation, and ensure the platform aligns with the company's strategic business objectives. This position is crucial for building a culture of Responsible AI.Major Responsibilities:* Platform Architecture and Strategy: Lead the architectural design, development, and long-term roadmap for the organization's AI/ML platform. Define the vision, strategy, and technical standards, ensuring alignment with overall business and technology goals.
- Cloud Infrastructure Design and Management: Architect and manage the cloud infrastructure supporting the MLOps platform, leveraging infrastructure-as-code (IaC) tools like Terraform. Optimize for scalability, security, cost-effectiveness, and high availability.
- Cross-Functional Collaboration and Stakeholder Management: Partner with data science, product management, engineering, and business teams to understand their requirements and ensure the MLOps platform effectively supports their needs. Effectively communicate technical concepts and strategies to both technical and non-technical audiences.
- Vendor and Technology Evaluation: Evaluate and select appropriate tools, technologies, and vendors to support the MLOps platform. Stay abreast of emerging trends and advancements in the MLOps landscape.
- AI/ML Reliability and Observability: Collaborate with the AI/ML reliability engineering team to design and implement components that ensure the platform’s operational reliability, observability, and fault tolerance.
- DevOps for Machine Learning Workloads: Build and maintain robust DevOps pipelines tailored for ML workflows, enabling automated model training, testing, deployment, and monitoring.
- Agentic AI Platform Engineering: Build and operationalize intelligent agent frameworks that autonomously monitor, diagnose, and respond to system events, leveraging feedback-driven learning, observability, and orchestration to improve platform resilience and efficiency.
Education & Experience:* Bachelor's degree preferred
- Master's degree in Computer Science, Data Science, AI, or related field preferred
- 7+ years of experience in Software Engineering, Systems Engineer, Dev Ops, or related role required
Benefits
HCA Healthcare, offers a total rewards package that supports the health, life, career and retirement of our colleagues. The available plans and programs include:
- Comprehensive benefits for medical, prescription drug, dental, vision, behavioral health and telemedicine services
- Wellbeing support, including free counseling and referral services
- Time away from work programs for paid time off, paid family leave, long- and short-term disability coverage and leaves of absence
- Savings and retirement resources, including a 401(k) Plan with a 100% match on 3% to 9% of pay (based on years of service), Employee Stock Purchase Plan, flexible spending accounts, preferred banking partnerships, retirement readiness tools, rollover support and financial wellbeing counseling
- Education support through tuition assistance, student loan assistance, certification support, dependent scholarships and a partnership with Galen College of Nursing
- Additional benefits for fertility and family building, adoption assistance, life insurance, supplemental health protection plans, auto and home insurance, legal counseling, identity theft protection and consumer discounts
Learn more about Employee Benefits
Note: Eligibility for benefits may vary by location.
HCA Healthcare has been recognized as one of the World's Most Ethical Companies® by the Ethisphere Institute more than ten times. In recent years, HCA Healthcare spent an estimated $3.7 billion in cost for the delivery of charitable care, uninsured discounts, and other uncompensated expenses.
"There is so much good to do in the world and so many different ways to do it."- Dr. Thomas Frist, Sr.
HCA Healthcare Co-Founder
If you find this opportunity compelling, we encourage you to apply for our Senior Staff MLOps Engineer opening. We promptly review all applications. Highly qualified candidates will be directly contacted by a member of our team. We are interviewing - apply today!
We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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 11,520 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At HCA Healthcare, 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 in Demand for This Role
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 171 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
HCA Healthcare AI Hiring
HCA Healthcare has 2 open AI roles right now. They're hiring across MLOps Engineer. Positions span Nashville, TN, US, Nashville, TN.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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.