Interested in this AI/ML Engineer role at TEEMA?
Apply Now →Skills & Technologies
About This Role
6\-8 month assignment for experienced AI Engineer with possibility of extension
Full time Monday through Friday must work 8 am to 5 pm Pacific Standard Time. Must be based in United States.
Job Summary:
The AI Engineer is responsible for building, testing, and deploying AI\-powered solutions that address real\-world healthcare challenges within our PACE (Program of All\-Inclusive Care for the Elderly) program. The AI Engineer will leverage enterprise AI models to not build or fine\-tune them to create intelligent applications such as participant context engines, retrieval\-augmented generation (RAG) pipelines, and agentic workflows. The AI Engineer will stay current with the rapidly evolving AI landscape and translate emerging capabilities into production\-ready solutions for our business. The AI Engineer collaborates effectively with colleagues and stakeholders to promote WelbeHealth values, team culture, and mission.
Job Responsibilities:
- Enterprise AI Application Development: Design, build, and deploy AI\-powered applications using enterprise LLMs (OpenAI, Anthropic Claude, Google Gemini).
- Translate PACE business requirements such as building rich participant context into production\-ready AI solutions.
- RAG Pipeline Engineering: Architect and implement retrieval\-augmented generation (RAG) systems that ground AI responses in WelbeHealth's proprietary data, ensuring accuracy, relevance, and compliance with healthcare data standards.
- Hands\-On Prototyping \& Delivery: Own the full development lifecycle for new AI use cases from ideation and rapid POC development through validation, iteration, and production deployment.
- Agentic Framework Development: Research and build agentic AI workflows (using frameworks such as LangGraph, LangChain, or Copilot Studio) that evolve our systems toward autonomous, goal\-oriented agents capable of handling complex multi\-step healthcare processes.
- Secure Cloud Deployment: Architect and deploy AI services within private cloud environments (primarily Azure; AWS as needed), utilizing Docker containers, private endpoints, managed identities, and secure VNET configurations.
- Multi\-Model Orchestration: Evaluate and integrate across the frontier model landscape, selecting the right model for each use case based on performance, cost, latency, and compliance requirements.
- Operational Excellence: Implement AIOps and MLOps best practices monitoring, versioning, automated testing, and CI/CD pipelines to ensure all AI applications are reliable, scalable, and maintainable.
- Technology Scouting: Continuously evaluate emerging AI tools, techniques, and model releases.
- Proactively recommend new approaches that can improve participant outcomes,
operational efficiency, or developer productivity.
- Must be willing and have the ability to work a varied schedule that may include evening nights, weekends and overtime.
- Complete all required documentation in a timely and accurate manner.
- Protect privacy and maintain confidentiality of all company procedures and information about team members, participants, and families.
- Follow WelbeHealth policies and procedures and participate in any required Quality Improvement activities, staff training and meetings.
- Communicate regularly with Supervisor and team regarding workload and priorities.
- Timely completion of all mandated trainings and education.
- Timely completion of all mandated occupational health screenings as needed.
- Exercises flexibility in performing assignments as business needs evolve.
- Other duties as assigned.
Skills:
Required Skills \& Experience:
- Minimum of three (3\) years of hands\-on experience in AI/ML engineering, applied AI development, or software engineering with a strong AI focus.
- Experience and competency working with people from diverse backgrounds and cultures.
- RAG \& Retrieval Systems: Demonstrated experience designing and deploying retrieval augmented generation pipelines, including vector databases, embedding strategies, chunking optimization, and retrieval evaluation.
- Enterprise LLM Integration: Proven ability to build applications on top of commercial LLM APIs (OpenAI, Anthropic, Google) including prompt engineering, structured output handling, function/tool calling, and context window management.
- Python: Advanced proficiency in Python for AI application development, API integration, and data pipeline construction.
- Cloud \& Containerization: Hands\-on experience with Azure AI services (AI Foundry, Managed Identities, Key Vault, private networking) and Docker\-based deployments in secure cloud environments.
- DevOps \& CI/CD: Proficiency with Azure DevOps (or equivalent) for building CI/CD pipelines that automate testing and deployment of AI applications.
- Agentic AI Patterns: Solid understanding of agentic architectures and frameworks such as LangChain, LangGraph, Semantic Kernel, or Copilot Studio.
- Agile Delivery: Experience working in Agile/Scrum environments with iterative development cycles and rapid POC delivery.
- Excellent organizational and communication skills.
- Ability to work independently with minimal supervision.
- Demonstrated ability to prioritize in a fast\-paced environment.
- Commitment to unlocking the full potential of our most vulnerable seniors.
Preferred Skills \& Experience:
- Healthcare Domain: Familiarity with HIPAA, PHI handling, and the compliance requirements unique to healthcare technology.
- PACE Program Knowledge: Understanding of the PACE model of care and how technology can improve participant outcomes and operational workflows.
- Model Evaluation: Experience benchmarking and comparing LLM providers across dimensions such as accuracy, cost, latency, and safety.
- AWS: Secondary cloud experience with AWS AI/ML services.
Education:
Required Education:
- Bachelor’s Degree required in Computer Science, AI, or Computer Engineering.
Preferred Education:
- Master’s Degree in the above.
\#Workwolf
Salary Context
This $187K-$249K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TEEMA, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($218K) sits 21% above the category median. Disclosed range: $187K to $249K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TEEMA AI Hiring
TEEMA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Seattle, WA, US, San Diego, CA, US. Compensation range: $130K - $249K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.