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About This Role
Join the Texas Health and Human Services Commission (HHSC) and be part of a team committed to creating a positive impact in the lives of fellow Texans. At HHSC, your contributions matter, and we support you at each stage of your life and work journey. Our comprehensive benefits package includes 100% paid employee health insurance for full\-time eligible employees, a defined benefit pension plan, generous time off benefits, numerous opportunities for career advancement and more. Explore more details on the Benefits of Working at HHS webpage.
Functional Title: AI Engineer
Job Title: Systems Analyst VII
Agency: Health \& Human Services Comm
Department: Chief Technology Office EI 3b
Posting Number: 19981
Closing Date: 10/10/2026
Posting Audience: Internal and External
Occupational Category: Computer and Mathematical
Salary Range: $8,488\.33\- $11,666\.66
Pay Frequency: Monthly
Salary Group: TEXAS\-B\-29
Shift: Day
Additional Shift: Days (First)
Telework:
Travel:
Regular/Temporary: Regular
Full Time/Part Time: Full time
FLSA Exempt/Non\-Exempt: Exempt
Facility Location:
Job Location City: AUSTIN
Job Location Address: 701 W 51ST ST
Other Locations:
MOS Codes: 0171,8848,8858,181X,182X,1D7X1,255A,255S,25B,25D,25H,26B,62E,681X,682X,781X,CTI,CTM,CTR,CYB10,CYB11
ISM,IT,Z Prefix
This position is open to U.S. Citizens and permanent residents.
This onsite role requires the selected candidate to work from an HHS office in Austin, Texas.
Performs highly complex computer systems analysis and AI engineering work for the CTO team. Work involves designing, engineering, configuring, and implementing data pipelines, AI agents, orchestration workflows, retrieval patterns, reusable AI services, secure integrations, and automation capabilities that support enterprise modernization and responsible AI innovation. The position helps translate business and technical needs into scalable AI\-enabled solutions by preparing governed data sources, developing reusable components, evaluating model and prompt performance, supporting secure access controls, and documenting repeatable implementation patterns. The position also supports responsible AI practices, solution monitoring, technical standards alignment, and collaboration with architecture, cloud, data, security, privacy, and application teams to deliver reliable, maintainable, and agency\-owned enterprise technology capabilities.
Works under general supervision, with moderate latitude for the use of initiative and independent judgment. Essential Job Functions (EJFs):
Percentage
Description
45%
Designs, develops, and implements AI agents, orchestration workflows, reusable AI services, prompt management solutions, API integrations, and automation components using approved cloud services, Python frameworks, and AI toolchains to support modernization, service delivery improvement, and responsible AI innovation.
30%
Designs and modifies secure data ingestion structures, retrieval\-augmented generation (RAG) vector stores, embeddings, semantic search patterns, metadata strategies, and automated processing pipelines to provide AI solutions with clean, structured, governed, and access\-controlled agency data.
15%
Evaluates, tests, and tunes AI model performance, agent behavior constraints, prompt effectiveness, retrieval quality, guardrails, auditability, and solution reliability to support security, privacy, compliance, accessibility, and approved system requirements.
5%
Prepares technical documentation, implementation notes, architecture diagrams, data flow diagrams, operational runbooks, and decision support materials; provides technical guidance to staff and project teams on AI integration, reuse, supportability, and secure modernization practices.
5%
Performs other duties as assigned.
Knowledge, Skills and Abilities (KSAs):
- Knowledge:
- + Thorough knowledge of Python programming and object\-oriented software patterns.
+ Thorough knowledge of modern AI frameworks, such as LangChain, LlamaIndex, and CrewAI, and cognitive orchestration architecture patterns.
+ Thorough knowledge of AI platforms and services offered by cloud service providers, such as Azure, AWS, and Google.
+ Thorough knowledge of vector and graph databases, such as Pinecone, Chroma, pgvector, and Neo4j, text embeddings, and semantic index optimization.
+ Thorough knowledge of enterprise data security regulations, data masking, and access management within state processing systems.
- Skills:
- + Strong skill in configuring secure ETL/ELT pipelines to structure, clean, and securely ingest large sets of semi\-structured and unstructured data.
+ Strong skill in building stateful, autonomous AI agents, establishing logic guardrails, and managing programmatic prompt lifecycles.
+ Strong skill in debugging asynchronous code flows, multi\-threaded worker routines, and API call limits within complex codebases.
- Abilities:
- + Ability to systematically benchmark AI systems for precision, context recall, safety, and toxicity using automated evaluation tools.
+ Ability to translate multi\-agent AI patterns into clear technical flowcharts and data maps
+ Ability to provide technical guidance and support developers through complex pipeline refactoring assignments.
Registrations, Licensure Requirements or Certifications:
None
Initial Screening Criteria:
- Graduation from an accredited four\-year college or university with major coursework in computer science, data engineering, artificial intelligence, or a related field. Experience may substitute for education on a year\-for\-year basis.
- Minimum of 4 years of experience in data engineering, programming, or supporting AI or LLM\-based solutions.
- preferred to have experience with AI or ML
Review our Tips for Success when applying for jobs at DFPS, DSHS and HHSC.
Active Duty, Military, Reservists, Guardsmen, and Veterans:
Military occupation(s) that relate to the initial selection criteria and registration or licensure requirements for this position may include, but not limited to those listed in this posting. All active\-duty military, reservists, guardsmen, and veterans are encouraged to apply if qualified to fill this position. For more information please see the Texas State Auditor’s Job Descriptions, Military Crosswalk and Military Crosswalk Guide at Texas State Auditor's Office \- Job Descriptions.
ADA Accommodations:
In compliance with the Americans with Disabilities Act (ADA), HHSC and DSHS agencies will provide reasonable accommodation during the hiring and selection process for qualified individuals with a disability. If you need assistance completing the on\-line application, contact the HHS Employee Service Center at 1\-888\-894\-4747\. If you are contacted for an interview and need accommodation to participate in the interview process, please notify the person scheduling the interview.
Pre\-Employment Checks and Work Eligibility:
Depending on the program area and position requirements, applicants selected for hire may be required to pass background and other due diligence checks.
HHSC uses E\-Verify. You must bring your I\-9 documentation with you on your first day of work. Download the I\-9 Form
Telework Disclaimer:
This position may be eligible for telework. Please note, all HHS positions are subject to state and agency telework policies in addition to the discretion of the direct supervisor and business needs.
Salary Context
This $101K-$139K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Texas Health and Human Services Commission, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($120K) sits 44% below the category median. Disclosed range: $101K to $139K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Texas Health and Human Services Commission AI Hiring
Texas Health and Human Services Commission has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $129K - $139K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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