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About This Role
At BlueCross BlueShield of Tennessee, we’re building the foundation for an AI‑enabled future—one where our teams work smarter, our operations move faster, and our 3\.4 million members experience a more responsive, reliable, and affordable healthcare journey.
We’re hiring a Senior AI Workflow Engineer to help drive this work within Commercial Finance. In this role, you’ll be embedded directly with our finance teams (including underwriting) to help them imagine what’s possible beyond today’s tooling.
To do that, you’ll need:
- Experience with Generative AI, LLM integration, or agent framework
- Understanding of APIs, orchestration, and integration patterns
- Proficiency in Python and associated AI / ML libraries and frameworks
Our ideal candidates will also bring experience in healthcare (other payers, hospital systems, health tech companies) and/or experience working with finance teams.
If you’re inspired by our mission, we’d love to hear from you!
Notes:
- While this is a fully remote role, a final onsite interview at our Chattanooga, TN, headquarters will be required.
- Sponsorship is not available for this role.
Job Responsibilities
Process Discovery \& Business Partnership
- Partner with security and legal teams to ensure all AI solutions are compliant with healthcare regulations, including HIPAA, and align with internal data security and privacy policies.
- Conduct qualitative and quantitative analysis to define clear business requirements and translate operational challenges into actionable workflow designs.
AI Workflow Design \& Engineering
- Architect and deploy AI\-enabled workflows integrating internal systems, APIs, and third\-party platforms.
- Incorporate AI components such as LLM agents and retrieval pipelines.
Adoption, Training \& Change Leadership
- Present workflow solutions to stakeholders and lead training sessions.
- Act as an evangelist for AI adoption across the enterprise and contribute to AI playbooks and operational standards.
- Establish telemetry for performance, drift, and cost.
- Troubleshoot issues and identify issues and opportunities to improve workflow efficiency and additional automation.
Job Qualifications
*Education*
- Bachelor’s degree in STEM (Science, Technology, Engineering or Math) or related field or equivalent work experience required. Master’s or PhD degree is a plus
*Experience*
- 4\+ years software engineering experience including automation pipelines or an equivalent combination of education, training, and demonstrated skills.
- Strong proficiency in Python; experience with cloud platforms.
- Understanding of APIs, orchestration, and integration patterns.
- Experience with Generative AI, LLM integration, or agent frameworks.
- Strong communication and stakeholder\-collaboration skills.
*Skills/Certifications*
- Advanced proficiency in Python and associated AI/ML libraries and frameworks.
- Deep experience with LLMs, NLP, and GenAI frameworks (LangChain, HuggingFace, LlamaIndex).
- Familiarity with containerization (Docker, Kubernetes) and microservices architecture.
- Basic analytical and problem\-solving skills.
- Good verbal and written communication skills. Ability to document work clearly.
- Strong business acumen and ability to translate strategic priorities into technical requirements.
- Highly organized, reliable, capable of managing multiple complex tasks, demonstrating an exceptional work ethic and strategic thinking.
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
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BlueCross BlueShield of Tennessee, 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 $218,750 based on 3,817 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 $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; 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. Based in Chattanooga, TN, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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