AI / ML Engineer

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : AWS Machine Learning Good to have skills : Data Science Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education Role Summary / Description AI Powered Tech Talent Engineer role in AI/ML Computational Science focused on hands-on development, integration, testing, and deployment of scientific AI, computational modeling, optimization, and ML-enabled engineering components on Amazon Web Services (AWS). You are expected to be a strong hands-on individual contributor who delivers assigned solution components, collaborates with project teams, and grows into broader technical ownership over time. The role supports computational science programs by building practical AI/ML components, scientific data workflows, reusable code assets, model pipelines, and cloud-native integrations under guidance from senior engineers and architects. Key Responsibilities Build AI/ML computational science components that support scientific data ingestion, preprocessing, simulation result handling, feature engineering, model training, inference, deployment, and monitoring. Design and implement assigned pieces of scientific AI, data engineering, optimization, surrogate modeling, simulation analytics, and ML workflow solutions. Write clean, tested, reusable Python, SQL, notebook, API, workflow orchestration, and cloud-native code aligned to project standards. Work with senior engineers, data scientists, domain experts, cloud engineers, architects, and delivery leads to ensure components integrate clearly with the broader solution. Support data validation, model evaluation, experiment tracking, documentation, reproducibility, testing, debugging, and implementation handover activities. Create reusable technical assets such as notebooks, data preparation scripts, validation utilities, model training templates, API wrappers, deployment scripts, and documentation. Participate in design discussions and contribute technical observations on feasibility, implementation constraints, edge cases, and quality risks. Help assemble supporting evidence for recommended AI/ML computational science solutions, including experiment outputs, model metrics, data quality checks, and technical notes. Keep developing skills in scientific AI, generative AI, agentic workflows, digital twins, MLOps, optimization, and cloud-native computational engineering. Required Qualifications Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.