Principal AI & Machine Learning Engineer, Spring, Texas, Onsite

Principal AI & Machine Learning Engineer This role has been designed as "Onsite" with an expectation that you will primarily work from an HPE office. Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today's complex world. Our culture thrives on finding new and better ways to accelerate what's next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. We are looking for an experienced Principal AI Engineer to drive the design, development, and deployment of AI/ML-powered applications. Candidate should have strong hands-on experience in application development, lead and mentor a team of AI developers, define best practices, and deliver scalable, production grade AI solutions aligned with business goals. Location: Spring, Texas Onsite daily work required Key Responsibilities Design, develop, and deploy AI applications, microservices, and APIs on Kubernetes-based infrastructure, ensuring scalability, reliability, and performance across development, staging, and production environments. Build and maintain end-to-end AI pipelines covering deployment, monitoring, versioning, and continuous improvement using modern MLOps/AIOps tools and practices. Lead and mentor a team of AI/ML engineers, conduct code reviews, and define best practices. Continuously evaluate and adopt emerging AI tools, frameworks, LLM technologies, and open-source solutions to enhance platform capabilities and team productivity. Collaborate closely with Business Analysts, Architect and technical teams to align AI engineering efforts with business objectives and ensure secure, compliant solutions. Establish and maintain technical documentation, deployment runbooks and SOPs Required Qualifications 10+ years of hands-on experience in software engineering, with a strong focus on AI/ML application development and deployment. Expertise in Kubernetes – container orchestration, Helm charts, pod management, scaling, and troubleshooting. Strong experience with MLOps/AIOps tools and practices (e.g., MLflow, Kubeflow, Airflow, model registries, monitoring frameworks). Hands-on experience with cloud platforms – Azure, AWS, or GCP, including their AI services. Strong programming skills in Python; familiarity with FastAPI, Flask, or similar frameworks is mandatory. Hands-on experience with CI/CD pipelines and tools such as GitOps, Docker, Jenkins, or GitHub Actions. Lead and mentor development teams, drive delivery, and manage technical priorities. Experience working with Agentic and GenAI frameworks and vector databases etc. Experience with observability and monitoring tools (Prometheus, Grafana, OpenTelemetry) for AI workloads. Good understanding of AI security, responsible AI principles, and governance frameworks. Education Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or a related field. The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level. – United States of America: Annual Salary USD 152,000 - 349,000 in TexasThe listed salary range reflects base salary. Variable incentives may also be offered.