Foundry Automation ML Engineer

Job Details: Job Description: Intel Foundry Automation (IFA) is looking for a highly motivated ML Engineer who is passionate about Productizing AI powered end-to-end solutions for its Silicon factories while working at the intersection of machine learning, software engineering, and cloud computing. As a Machine Learning Engineer in IFA, you will play a pivotal role in building cutting-edge machine learning workflows and infrastructure that enable Foundry to produce AI models and sustain them in production. This position offers an exciting opportunity to contribute to scalable AI solutions, automate ML pipelines, and empower Intel's commitment to innovation. Your work will directly impact critical advancements in data analytics, computer vision, early/inline detection, reducing process variability and accelerating Yield ramps. What You'll Do: You will work with a team of experienced engineers to build, deploy, and scale ML-powered services on our cloud infrastructure including: Design, build, and maintain scalable ML pipelines for data processing, model training, and inference in an on-prem cloud environment. Prepare and process large-scale datasets for training and deploying ML models. Develop and deploy APIs and microservices that interact with various components of the ML application stack. Monitor, debug, and optimize deployed ML models to enhance performance and reliability. Conduct programming, testing, and documentation to ensure high-quality deployment of machine learning solutions. Work with containerization technologies like Docker and orchestration systems like Kubernetes to package and scale ML services. Leverage Intel manufacturing's cloud-native ML platforms based on Kubernetes/Rancher to accelerate the deployment lifecycle. Implement MLOps best practices for model versioning, monitoring, and continuous integration/continuous deployment (CI/CD). Collaborate with software developers, data scientists, and DevOps engineers to integrate ML capabilities seamlessly into our products. Optimize the performance, latency, and cost of our deployed ML models. Qualifications: You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the requirements and are considered a plus factor in identifying top candidates. Minimum Qualifications: A Bachelors degree in Computer Science, Computer Engineering, Data science, Computational Physics, or Applied AI with 5 years of industry experience. OR Masters degree in Computer Science, Computer Engineering, Data science, Computational Physics, or Applied AI with 3 years of industry experience. OR Ph.D. in Computer Science, Computer Engineering, Data science, Computational Physics, or Applied AI with 6 months of industry experience. The qualifications listed below must meet the required years of experience associated with the candidate’s degree level: Programming skills, particularly in Python with robust unit testing Experience with software engineering principles (e.g., data structures, algorithms, object-oriented design). Experience working with Image analytics libraries like OpevCV and machine learning frameworks like pytorch, Scikit-learn, or TensorFlow etc. Strong understanding of Algorithm Optimization for CPUs and GPUs, AI fundamentals, and/or deep learning models. Experience with MLOps, CI/CD knowledge and processes, Kubernetes, and/or ML automation pipelines. Proven ability to develop and deploy ML models. Solid foundation in machine learning algorithms, including supervised and unsupervised learning, deep learning, and/or reinforcement learning, and/or Bayesian analysis. Preferred Qualifications: 1 years of experience solving applied problems in semiconductor manufacturing or design. Experience in Deep learning or Image analytics Demonstrated ability to address complex use cases across various domains. Strong communication and problem-solving skills, with experience dr ... (truncated, view full listing at source)