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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: Cloud \& AI Platform Engineer
Job Title: Systems Analyst VII
Agency: Health \& Human Services Comm
Department: Data Analytics Supp DSHS COVID
Posting Number: 19656
Closing Date: 10/02/2026
Posting Audience: Internal and External
Occupational Category: Computer and Mathematical
Salary Range: $8,488\.33\- $10,812\.40
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
SHARP is seeking a senior‑level Cloud \& AI Platform Engineer (Systems Analyst VII) to provide deep technical expertise and architectural guidance for our AWS\-based analytics and AI platform. This position focuses on hands‑on cloud engineering, platform reliability, forward‑looking solution design, and operational support, while guiding technical decisions across cloud, data, and AI use cases.
The Cloud \& AI Platform Engineer serves as a trusted technical advisor and subject‑matter expert, working collaboratively with data engineers, analysts, systems administrators, and stakeholders.
Selected candidate must be willing to work from an HHS office located in Austin, Texas designated by the hiring manager.
Essential Job Functions (EJFs):
Attends work on a regular and predictable schedule in accordance with agency leave policy and performs other duties as assigned.
- 20% Responsible for the SHARP AWS environment, including design, configuration, monitoring, maintenance, and support of cloud infrastructure. Ensures availability, performance, scalability, security, and cost efficiency of AWS services supporting SHARP applications and data platforms. This includes configuration, implementation, and support of SHARP networking infrastructure. Participates in incident response and operational issue resolution as needed.
- 15% Participate in agile data engineering development and support the team with the database design and implementation based on data pipeline requirements.
- 10% Supports the implementation and operational enablement of artificial intelligence (AI) and advanced analytics solutions within SHARP. Provides cloud infrastructure, integration support, and operational readiness to ensure AI solutions are securely deployed, monitored, and scalable.
- 10% Develops and maintains Python scripts and automation to support both operational tasks and data engineering tasks, incl integrations, monitoring, and platform efficiencies. Assists development and data teams with Python\-based operational tooling as needed.
- 10% Perform as a backup administrator for other applications (non\-database) that are part of the platform.
- 10% Collaborate with various internal and external stakeholders to formulate policies, procedures, and standards related to data management.
- 10% Perform timely resolution of incidents reported in production and ensure minimal disruption to operations.
- 10% Participate in meetings, report performance metrics, and support project management activities, including but not limited to timekeeping, status reporting, and presentations.
- 5% Other duties as assigned
Knowledge Skills Abilities:
- Knowledge of AWS cloud architecture and services used to support secure, scalable, and cost‑efficient production platforms
- Knowledge of networking principles including TCP/IP, routing, firewalls, and cloud network design
- Knowledge of relational database technologies, including AWS RDS (PostgreSQL, MySQL, or similar)
- Knowledge of data engineering concepts, data pipelines, and analytics platform requirements
- Knowledge of governance, policies, and standards related to data management and platform operations
- Knowledge of operational support practices, including incident response and production support
- Skilled in designing, deploying, and supporting cloud infrastructure in enterprise environments
- Skilled in advanced Python scripting and automation for operational support, integrations, and monitoring
- Skilled in Linux system administration, troubleshooting, and performance tuning
- Skilled in diagnosing and resolving production issues using monitoring and logging tools
- Skilled in collaborating with cross‑functional technical teams in agile development environments
- Skilled in developing and maintaining technical documentation, standards, and procedures
- Ability to serve as a trusted technical advisor and subject matter expert for cloud, data, and AI platforms
- Ability to ensure reliability, security, scalability, and cost efficiency of analytics and AI systems
- Ability to support the operational enablement of AI and advanced analytics solutions
- Ability to analyze complex technical problems and implement effective, sustainable solutions
- Ability to work independently while coordinating effectively with internal and external stakeholders
Registrations, Licensure Requirements or Certifications:
AWS Certification is preferred.
Initial Screening Criteria:
Required:
- 6\+ years of experience with AWS
- 8\+ years experience with Python
- 8\+ years of experience with Linux systems
- 4\+ years experience with PostgreSQL, MySQL, SQL or similar.
Preferred:
- 1\+ Years Experience supporting analytics or AI platforms at enterprise scale.
- 1\+ Years Experience in public‑sector or regulated environments.
- 1\+ Years Experience supporting or enabling AI/ML or advanced analytics workloads in cloud environments.
- 1\+ Years Snowflake administration, performance, and support.
Additional Information:
Any employment offer is contingent upon available budgeted funds. The offered salary will be determined in accordance with budgetary limits and the requirements of HHSC Human Resources Manual.
- Selected candidates must be legally authorized to work in the U.S. without sponsorship.
- Selected candidate must be willing to work from an HHS office located in Austin, designated by the hiring manager.
\#LI\-MH1
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-$129K 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 ($115K) sits 46% below the category median. Disclosed range: $101K to $129K.
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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