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About This Role
Job Description:
Job Title: Principal AI Engineer
Location: Springfield, Missouri
Department: Information Technology
Employment Type: Full-time
Job Summary: Join a compassionate, collaborative team making a measurable difference by delivering AI-driven analytics that empower business decisions and improve outcomes for stakeholders. This role offers the opportunity to lead high-impact AI initiatives, mentor cross-functional teams, shape enterprise AI strategy, work with cutting-edge LLM and Snowflake technologies, and build production-ready systems that increase data accessibility and drive faster insights. We seek a self-directed, systems-thinking engineer with strong communication skills and a passion for translating complex analytical needs into scalable technical solutions.
The Principal AI Engineer is the senior technical contributor within Analytics responsible for designing, developing, and implementing enterprise AI capabilities on the data platform with a focus on Snowflake integration. This hands-on role develops natural language–to–SQL solutions, semantic data models, and prototype applications that enable governed, conversational access to enterprise data, and guides their transition into scalable, production-ready solutions.
This position offers…
- Employee Assistance Program – 24/7 counseling services, legal assistance, & financial consultation for you and your household at no cost
- Mileage Reimbursement – Company paid for work functions requiring travel
- Employee Discounts – Hotels, Theme Parks & Attractions, College Tuition
- Workplace Culture - An environment cultivating employee wellbeing, valuing each individual's humanity, and actively promoting a healthy, joyful workforce
- Additional Perks & Benefits - Scroll down to bottom of this post to learn more Key Responsibilities:
- Lead the design and implementation of LLM-enabled analytics capabilities leveraging Snowflake and Cortex AI features
- Develop natural language–to–SQL solutions and conversational interfaces that allow business users to query governed enterprise data
- Collaborate with Data Engineering to design and maintain semantic data models that support reliable AI-driven query generation
- Build prototype AI applications to validate high-value business use cases and demonstrate new capabilities
- Guide the transition of prototypes into scalable, production-ready solutions and establish architectural patterns and best practices for LLMs with structured data
- Assist Data Engineering with prompt design, query-generation frameworks, evaluation approaches, and integration tasks
- Identify high-impact opportunities for AI across analytics, reporting, and decision-support workflows
- Evaluate emerging AI technologies, tools, and Snowflake platform capabilities for potential adoption
- Support development of standards for responsible, secure AI usage aligned with enterprise data governance
- Document system architecture, design decisions, and implementation approaches
- Provide technical guidance and mentorship to analytics and data engineering team members
- Function in accordance with operational guidelines and professional ethics; participate in staff development activities; perform other duties as assigned Education, Experience, and/or Credential Qualifications:
- Bachelor’s degree or equivalent experience in Computer Science, Data Engineering, Data Science, Information Systems, or related field
- 7+ years of experience in data, analytics, engineering, or applied AI roles
- Demonstrated experience building or prototyping AI/LLM-driven solutions in enterprise environments
- Experience with modern data warehouse platforms (Snowflake preferred)
- Proficiency in SQL and Python
- Experience with cloud platforms (AWS, Azure, or GCP) preferred
- Healthcare or regulated industry experience preferred but not required Additional Qualifications:
- Strong understanding of modern data warehouse architecture and analytics data modeling practices
- Experience designing natural language query interfaces, prompt engineering, context management, and grounding techniques for LLM systems
- Knowledge of semantic data modeling and data governance principles
- Ability to translate complex analytical questions into scalable technical solutions and communicate clearly to technical and non-technical stakeholders
- Self-directed problem solver with leadership and mentoring experience
- Successful completion of background check including criminal record, driving record, abuse/neglect, and fingerprint check required
- Current driver’s license, acceptable driving record, and current auto insurance required Physical Requirements
- Sedentary work: exerting up to 10 pounds of force occasionally and/or negligible force frequently to lift, carry, push, or pull objects; repetitive movements of hands, fingers, and arms for typing and/or writing during work shift
- Primarily sitting with occasional walking or standing for brief periods
Keywords: Principal AI Engineer, LLM, Snowflake, Cortex AI, natural language to SQL, semantic data modeling, data engineering, Python, SQL, enterprise AI
Position Perks & Benefits:
Paid time off: full-time employees receive an attractive time off package to balance your work and personal life
Employee benefits package: full-time employees receive health, dental, vision, retirement, life, & more
Top-notch training: initial, ongoing, comprehensive, and supportive
Career mobility: advancement opportunities/promoting from within
Welcoming, warm, supportive: a work culture & environment that promotes your well-being, values you as human being, and encourages your health and happiness
Brightli is on a Mission:
A mission to improve client care, reduce the financial burden of community mental health centers by sharing resources, a mission to have a larger voice in advocacy to increase access to mental health and substance user care in our communities, and a mission to evolve the behavioral health industry to better meet the needs of our clients.
As a behavioral and community mental health provider, we prioritize fostering a culture of belonging and connection within our workforce. We encourage applications from individuals with varied backgrounds and experiences, as we believe that a rich tapestry of perspectives strengthens our mission. If you are passionate about empowering local communities and creating an environment where everyone feels valued and supported, we invite you to join our mission-driven organization dedicated to cultivating an authentic workplace.
We are an Equal Employment Opportunity Employer.
Brightli is a Smoke and Tobacco Free Workplace.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Burrell Behavioral Health, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Burrell Behavioral Health AI Hiring
Burrell Behavioral Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Springfield, MO, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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