Job
Description
Key Skills: SCALA, Database, Python, Data Engineering, ML, AWS, Kubernetes, Docker Roles and Responsibilities: Strong technical and analytical skills for the development and verification of scalable and performant solutions for the existing and emerging ML operators on the target AMD products mentioned above. Ability to analyze/address potential performance issues and providing necessary fixes Maintaining the quality of the results while providing scalable solutions Effective communication/reporting of technical data with the worldwide team and internal engineering teams. Contributing to technical interactions with AMD customers Partner with data scientists to train, optimize, scale and support AI/ML models. Establish robust Data and ML engineering principles in the implementation of ML algorithms on the AWS platform. Translating functionality into scalable, tested, and configurable platform architecture and software. Ensure models are integrated into production systems with high reliability and performance. Design and conduct experiments to evaluate the performance of machine learning models. Iterate on models based on feedback and evolving business requirements. Build frameworks, reusable modules, and author best practices and standards to enable self-serve capabilities that enable data scientists to deploy solutions quickly and efficiently. Mentor and lead a team of Jr. Engineers in developing efficient and scalable solutions consistent with established Enterprise standards. Skills Required: 5+ years professional experience in implementing ML and Data Engineering at scale. 5+ years of experience with Python & SQL programming language. 5+ years of experience in building cloud native analytical solutions, on AWS (or similar). 3+ years designing, implementing, and optimizing machine learning algorithms and models using open-source machine learning frameworks such as TensorFlow, PyTorch, and XGBoost. 3+ years' experience in end-to-end ML implementation lifecycle including feature engineering, training, inference, model drift measurement, and model observability. 3+ years' experience with working in Dev/ML Ops model and industry deployment best practices using CI/CD tools and infrastructure as a code (e.g., Jenkins, Docker, Kubernetes). 3+ years' experience in building data engineering pipelines, and metric aggregation layers. Preferred Qualifications: Experience with the AWS platform, including SageMaker, Bedrock, etc. Hands-on experience working in scalable distributed computation frameworks like Spark or Dask. Expertise with working with open-source ML platforms & toolsets such as Kubeflow, Airflow, ML Flow or Feast is preferred. Experience in deploying supervised machine learning, time series modeling, CNNs, ensemble models. Exposure to deploying ML optimization models such as Pyomo/ IPOPT. Exposure in implementing Gen AI and/or NLP based solutions using LLMs. Experience in the state-of-the-art ML models/operators, their mapping on modern multicore architectures and related compilation technologies Understanding the general performance optimization techniques used in ML workloads, such as operator fusion, quantization Strong C/C++ programming experience and scripting skills (python/shell), being comfortable with both ISA-aware programming and providing necessary abstractions for scalable solutions Effective communication and problem-solving skills Education: Bachelor's Degree in related field
