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About This Role
BlueCross BlueShield of Tennessee is looking for an Associate MLOps Engineer to support data science teams in building, deploying, and operating machine learning solutions at scale. This is a hands\-on, individual contributor role focused on technical execution, continuous learning, and collaboration. You will work closely with data scientists and engineers in a modern production environment, with mentorship from experienced MLOps professionals.
What You’ll Bring:
- Experience using Python for scripting, data processing, or supporting machine learning workflows
- Experience working with a cloud\-based platform (e.g., AWS, Azure, or GCP) to develop, deploy, or support data or machine learning solutions
- Exposure to CI/CD practices, including Git\-based workflows, automated testing, builds, and deployments
- Understanding of the machine learning lifecycle, including experimentation, model versioning, and reproducibility (e.g., MLflow or similar tools)
- Foundational knowledge of data engineering concepts, such as data ingestion, transformation, validation, and storage
- Experience contributing to simple full\-stack applications, including:
+ Python\-based backend APIs
+ Basic front\-end views or dashboards to display data or model outputs
- Willingness to follow established patterns and best practices to help move ML solutions from prototype to production
Nice to Have
- Familiarity with OpenShift or Kubernetes, and working with containerized applications
- Experience building or maintaining infrastructure\-as\-code using Terraform (e.g., defining resources and managing environments)
- Exposure to Databricks for data engineering, analytics, or machine learning workflows
Why Join Us?
This fully\-remote role is ideal for someone who enjoys hands\-on technical work, is eager to develop modern MLOps practices, and wants to contribute to real\-world production systems. You’ll be joining a tax\-paying not\-for\-profit organization and your work will directly contribute to our mission – peace of mind through better health.
Note
- Final interviews onsite at our Chattanooga, TN headquarters are required for this role.
- Sponsorship is not available for this role.
Job Duties \& Responsibilities
- Model Deployment: Ensuring that machine learning models are deployed efficiently and reliably into production environments.
- Model Monitoring: Continuously monitoring the performance of models to detect issues like model drift and ensure they remain accurate and effective.
- Automation: Automating the machine learning pipeline, including tasks like data preprocessing, model training, and evaluation.
- Collaboration: Working closely with data scientists, software engineers, and IT operations to integrate machine learning models into business processes.
- Version Control and Governance: Managing version control for models and ensuring compliance with governance policies.
- Optimization: Identifying and implementing ways to improve the performance and scalability of ML systems Responsibility
- Exploring cloud tools and technologies that assist data science with implementing their usecases.
Job Qualifications
*Education*
- Bachelor’s degree in computer science or equivalent work experience required. Equivalent experience is defined as 4 years of professional work experience in a corporate environment.
*Experiences*
- 2 years \- Experience in software engineering and analytics technology (academic experience included)
- Experience handling large datasets to build data pipelines.
- Experience writing SQL and using Data Visualization tools.
- Experience solving complex problems and independently developing solutions.
*Skills/Certifications*
- Programming: Demonstrated proficiency in languages like Python or similar languages
- Data Engineering: Strong understanding of data processing and storage solutions.
- Problem\-Solving: Ability to troubleshoot issues in ML models and infrastructure.
- Ability to work independently with minimal supervision or function in a team environment sharing responsibility, roles, and accountability.
- Excellent oral and written communication skills
- Strong interpersonal and organizational skills
Number of Openings Available
1Worker Type:
EmployeeCompany:
BCBST BlueCross BlueShield of Tennessee, Inc.Applying for this job indicates your acknowledgement and understanding of the following statements:
BCBST will recruit, hire, train and promote individuals in all job classifications without regard to race, religion, color, age, sex, national origin, citizenship, pregnancy, veteran status, sexual orientation, physical or mental disability, gender identity, or any other characteristic protected by applicable law.
Further information regarding BCBST's EEO Policies/Notices may be found by reviewing the following page:
BCBST's EEO Policies/Notices
BlueCross BlueShield of Tennessee is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at BlueCross BlueShield of Tennessee via\-email, the Internet or any other method without a valid, written Direct Placement Agreement in place for this position from BlueCross BlueShield of Tennessee HR/Talent Acquisition will not be considered. No fee will be paid in the event the applicant is hired by BlueCross BlueShield of Tennessee as a result of the referral or through other means.
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 BlueCross BlueShield of Tennessee, 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. Entry-level AI roles across all categories have a median of $97,380.
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.
BlueCross BlueShield of Tennessee AI Hiring
BlueCross BlueShield of Tennessee has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in Chattanooga, TN, 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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