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AI/ML Data Reliability Engineer – AIOps
Job in
Austin, Travis County, Texas, 78716, USA
Listed on 2026-01-01
Listing for:
Judge Group, Inc.
Full Time
position Listed on 2026-01-01
Job specializations:
-
IT/Tech
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Location: Austin, TX – 100% On-Site
Duration: 12+ Months (Contract)
Citizenship Requirement: U.S. Citizen, Permanent Resident or EAD
Position OverviewWe are seeking a AI/ML Data Reliability Engineer to join our AIOps team
, driving the design and delivery of intelligent, automated solutions leveraging modern AI and ML technologies. This team’s mission is to explore and implement the latest advancements in Generative AI, ML, and automation tools to streamline manual workflows and develop innovative internal and external applications.
Skills & Qualifications
- 5+ years of experience in Python development, with strong background in AI/ML or data engineering.
- Proven leadership experience guiding technical teams or projects in an AI/ML or software engineering environment.
- Hands‑on experience integrating or building applications using LLMs (GPT, Claude, etc.).
- Proficiency with Google Cloud Platform and related AI/ML services.
- Experience with Git Hub Actions, Splunk, Grafana, and Mongo
DB. - Strong understanding of AIOps concepts, automation frameworks, and observability tools.
- Ability to balance strategic design thinking with hands‑on development and delivery.
- Experience with PCF (Pivotal Cloud Foundry).
- Prior exposure to GenAI platform adoption or evaluation within enterprise environments.
- Familiarity with data pipelines, API development, and AI model lifecycle management.
- Lead a team of 4 Data Engineers, providing technical direction, architectural guidance, and hands‑on coding support (does not require people management).
- Design and build Python‑based AI/ML tools, both in‑house and integrated with enterprise platforms.
- Develop applications powered by LLMs (e.g., GPT, Claude) – embedding AI models at the foundational layer through to end‑user application interfaces.
- Collaborate with stakeholders to identify opportunities where AI/ML can enhance operational efficiency and enable new capabilities.
- Integrate AI solutions with existing infrastructure using Google Cloud Platform as the primary environment.
- Leverage tools such as Splunk, Grafana, and Git Hub Actions for observability, automation, and continuous integration.
- Partner with cross‑functional teams to adopt or extend external AI platforms and develop creative solutions tailored to organizational needs.
- Participate in end‑to‑end delivery of GenAI and ML applications – from experimentation to production deployment.
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