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About This Role
DEPARTMENT: Data Insights and Innovation
JOB TITLE: MLOps Engineer
JOB CODE: MLOE
REPORTS TO: GenAI Engineering Lead
FLSA STATUS: Exempt
EMPLOYMENT TYPE: Full\-Time
JOB PURPOSE:
This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion).
The MLOps Engineer is responsible for closing the gap between machine learning models development and their operational deployment. This role ensures that machine learning models are efficiently running in the production environment and are continuously monitored for performance.
The MLOps Engineer contributes to Arbitration Forums AI\-powered portfolio of products and services by enhancing the scalability and reliability of machine learning applications. This role works closely with data scientists, AI engineers, software development, and DevOps teams to automate and streamline the model lifecycle, from development to deployment and monitoring.
DEPARTMENTAL EXPECTATION OF EMPLOYEE
- Adheres to AF Policy and Procedures and the AF IPAAL Values and TRI Model
- Acts as a role model within and outside AF.
- Performs duties as workload necessitates.
- Maintains a positive and respectful attitude.
- Communicates regularly with the departmental leader about department issues.
- Demonstrates flexible and efficient time management and ability to prioritize workload.
- Consistently reports to work on time, prepared to perform duties of the position.
- Meets Department productivity standards.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Design, implement, and maintain machine learning pipelines and workflows for the continuous deployment and integration of machine learning models. Optimize the pipelines for scalability, efficiency, and cost\-effectiveness.
- Collaborate with data scientists and AI engineers to understand model requirements and optimize deployment processes.
- Automate the training, testing, and deployment processes for machine learning models.
- Establish and enforce best practices for version control, documentation, and code quality in ML projects.
- Monitor model performance and optimize algorithms for efficiency.
- Conduct regular maintenance and updates to deployed models.
- Collaborate with cross\-functional teams to integrate machine learning solutions into business processes and applications.
- Work with go to market, product management, and IT functions as well as stakeholders in AF and its members to identify the optimal methods for model rollout and adoption.
- Maintain and optimize the cloud\-based machine learning infrastructure and make recommendations for improvements.
- Manage and allocate resources effectively, including computer power and storage for model inference.
- Develop practices and utilize tools for data validation, model testing, and versioning.
- Troubleshoot and resolve machine learning operational issues.
- Document processes, workflows, and best practices for ML Operations.
- Provide technical leadership and mentorship to junior data team members.
ADDITIONAL DUTIES AND RESPONSIBILITIES
- Support data observability efforts to ensure the data continuum and enforce governance standards.
- Other duties as assigned by manager or project needs.
QUALIFICATIONS
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, or a related field.
- Minimum of 6 years of experience in data science, machine learning, data management, data governance, or a related role.
- Minimum of 6 years as a MLOps Engineer or in a similar role.
Technical Skills:
- Working knowledge of cloud services (i.e., MS Azure, AWS, Google Cloud).
- Experience with AI tools, such as MS Azure ML, Snowflake, Databricks, CortexAI, Dataiku.
- Deep understanding of data science principles, algorithms, and tools.
- Strong knowledge of data governance, data security, and compliance practices.
- Proficiency in programming languages such as Python, R, or Java.
- Experience with containerization tools like Docker and orchestration tools like Kubernetes.
- Proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit\-learn.
- Working knowledge of CI/CD pipelines, DevOps practices, and automation frameworks.
- Deep understanding of data engineering concepts and tools.
- Familiarity with data visualization and reporting tools (e.g., Webfocus, Power BI).
Soft Skills:
- Excellent analytical and problem\-solving abilities.
- Strong communication and interpersonal skills to collaborate with cross\-functional teams.
- Ability to lead projects and mentor junior staff.
- Auto Insurance claims industry experience preferred.
AMERICANS WITH DISABILITY SPECIFICATIONS
PHYSICAL DEMANDS
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.
While performing the duties of this job, the employee is occasionally required to stand; walk; sit; use hands to finger, handle, or feel objects, tools, or controls; reach with hands and arms; climb stairs; balance; stoop, kneel, crouch or crawl; talk or hear; taste or smell. The employee must occasionally lift and/or move up to 25 pounds. Specific vision abilities required by the job include close vision, distance vision, color vision, peripheral vision, depth perception, and the ability to adjust focus.
WORK ENVIRONMENT
This is a fully remote position requiring reliable high\-speed internet access and a dedicated workspace.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
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 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Arbitration Forums Inc., 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 76 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Arbitration Forums Inc. AI Hiring
Arbitration Forums Inc. has 1 open AI role right now. They're hiring across MLOps Engineer. Based in Tampa, FL, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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