AI/ML Platform Engineer

Our client is seeking a highly skilled AI/ML Platform Engineer to build and maintain the infrastructure supporting their advanced artificial intelligence initiatives in Austin, Texas, US . This role is crucial for enabling data scientists and machine learning engineers to develop, train, and deploy models efficiently and at scale. You will be responsible for designing, implementing, and managing the end-to-end ML pipeline, including data ingestion, feature engineering, model training, and deployment infrastructure. The ideal candidate will have extensive experience with cloud platforms (AWS, Azure, GCP), containerization technologies (Docker, Kubernetes), MLOps tools, and big data technologies. You will work closely with research and engineering teams to optimize workflows and ensure the reliability and performance of the AI/ML platform.

Key Responsibilities: Design, build, and maintain scalable AI/ML infrastructure on cloud platforms (AWS, Azure, GCP). Develop and manage CI/CD pipelines for machine learning models. Implement and optimize data pipelines for AI/ML workloads. Deploy and manage containerized ML environments using Docker and Kubernetes. Implement MLOps best practices for model training, versioning, and deployment. Develop tools and frameworks to streamline the ML development lifecycle. Monitor and troubleshoot the performance and reliability of the AI/ML platform. Collaborate with data scientists and ML engineers to understand their infrastructure needs. Ensure the security and compliance of the AI/ML infrastructure. Evaluate and integrate new AI/ML technologies and tools. Provide technical expertise and support to internal teams.

Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 5+ years of experience in software engineering, with a focus on MLOps, data engineering, or cloud infrastructure. Proven experience with cloud platforms (AWS, Azure, or GCP) and their AI/ML services. Strong experience with containerization (Docker) and orchestration (Kubernetes). Proficiency in Python and scripting languages. Familiarity with MLOps tools and concepts (e.g., MLflow, Kubeflow). Experience with big data technologies (e.g., Spark, Hadoop) is a plus. Solid understanding of machine learning concepts and workflows. Excellent problem-solving and analytical skills. Strong communication and collaboration abilities. Experience with infrastructure as code tools (e.g., Terraform, Ansible) is desirable. Join a leading technology company at the forefront of AI innovation and play a key role in building the future of intelligent systems.