AI/ML Engineer

Job ID: TX-529601510

Hybrid/Local AI/ML Engineer (15+) with GitHub, Hyper-V, CI/CD, AWS/Azure/Google Cloud Platform, Python/Bash, data analytics, Agentic AI/MCP/RAG, GenAI, IaC, IDEs/STS, Scikit-learn/TensorFlow/PyTorch, Agile/Scrum/DevOps, MLOps, Terraform/Pulumi/CloudFormation, BMC Helix ticketing system experience

Location: Austin, TX (HHSC)
Duration: 8 Months
Position: 1 (2)
Position will be 3 days remote with 2 days (Tuesdays and Fridays) required to be onsite at the location listed above.
Program will only accept LOCAL ONLY candidates for this position.

Skills:
8 Required Proven ability to administer GitHub Enterprise Cloud
8 Required Proven ability to analyze and resolve complex issues
8 Required Supporting and training end users on all levels.
8 Required Hands-on experience with Continuous Integration Delivery models
3 Preferred Hands-on experience with large development projects using Agile methodology

Description:
Develops software solutions by studying information needs, conferring with users, and studying systems flow, data usage, and work processes. Investigates problem areas. Prepares and installs solutions by determining and designing system specifications, standards, and programming.
Texas Health and Human Services Commission requires the services of 1 Software Engineer 3, hereafter referred to as Candidate(s), who meets the general qualifications of Software Engineer 3, Applications/Software Development and the specifications outlined in this document for the Texas Health and Human Services Commission.

We are seeking an AI Engineer to drive innovation in our SDLC processes using artificial intelligence and automation. This role is ideal for an engineer passionate about automation and applying AI/ML techniques to improve reliability, observability, and operational workflows. The focus of this role is not to support external AI/ML product teams, but to internally develop AI-driven solutions that optimize SDLC processes, reduce toil, and increase automation maturity across the organization.

Key Responsibilities:
Design and implement AI/ML models that improve SDLC processes in domains, such as:
o Developer experience and productivity
o Intelligent test management using data analytics and predictive techniques
o Predictive infrastructure failure detection
o Agentic AI, MCP implementation, and RAG techniques
o Intelligent alerting and noise reduction
o Automated incident classification and root-cause analysis
o CI/CD optimization based on historical trends
o Using GenAI for IaC
o Any other innovative use-cases.
Work closely with Development, DevOps, and Infrastructure teams to identify automation opportunities and pain points.
Develop automation scripts and tooling to reduce manual tasks, operational efficiencies, and user experience.
Build, deploy, and maintain pipelines to train and continuously improve AI models for DevOps use-cases.
Collaborate with Infrastructure, Cloud, and DevOps teams to create architecture/design documents for proposed solutions.
Ensure operational reliability, scalability, and performance of AI-driven automation tooling.
Integrate AI solutions into monitoring.
Experience with Agile Scrum and DevOps methodologies
Experience working in Developer IDEs, such as Eclipse, IBM Rational Application Developer, STS, etc.
Create technical and design documentation, as required
Perform system analysis, troubleshooting, diagnosis and problem resolution. Analyze software for defects and performance tuning opportunities
GitHub Administration:
Manage repositories, branching strategies, and access control.
-Automate workflows using GitHub Actions or similar CI/CD tools.
-Maintain code quality and integration processes.
-Define and implement governance rules.
Other duties as assigned.

Required Skills & Qualifications:
Bachelor s degree in Computer Science, Engineering, or equivalent experience.
3+ years in Development/Automation roles.
Strong background in cloud-native infrastructure (AWS, Azure, or Google Cloud Platform).
Proficiency in automation and scripting (Python is preferred, Bash, etc.).
Solid understanding of CI/CD pipelines
Experience with cloud-native technologies
Experience applying AI/ML techniques to solve engineering problems (e.g., anomaly detection, classification, clustering).
Familiarity with Python machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
Good understanding of monitoring, logging, and observability tooling.

Preferred Skills:
Experience with anomaly detection, predictive analytics, or time-series forecasting.
Knowledge of MLOps practices (for internal AI models).
Experience integrating AI solutions into DevOps toolchains and platforms.
Familiarity with infrastructure as code (Terraform, Pulumi, CloudFormation).
Some working experience with Hyper-V Virtual Machine Management
Asset and service account management
BMC Helix ticketing system