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 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Role Summary / Description AI Powered Tech Talent Senior Engineer role in AI/ML Computational Science focused on designing, building, and integrating scalable scientific AI, simulation intelligence, computational modeling, optimization, and ML-enabled engineering solutions on Amazon Web Services (AWS). You are expected to lead a technical workstream, guide implementation choices, mentor engineers, contribute to solution design, and support delivery leadership within a larger program. The role converts computational science and engineering problems into practical AI/ML components, scientific data pipelines, model workflows, and reusable cloud-native patterns that support scalable client outcomes. Key Responsibilities Lead the design and build of AI/ML computational science components that support scientific data ingestion, simulation result processing, feature engineering, model development, deployment, and monitoring. Translate scientific, engineering, and business problems into practical ML, optimization, surrogate modeling, simulation analytics, and data engineering solution patterns. Develop production-quality Python, SQL, API, workflow orchestration, and cloud-native components that integrate with broader enterprise platforms. Work with technical architects, data scientists, domain experts, cloud engineers, product owners, and delivery leads to ensure solution components integrate cleanly with the wider system architecture. Guide junior engineers on implementation practices, code quality, testing, documentation, reproducibility, observability, and delivery readiness. Contribute to design reviews, technical decision logs, implementation plans, estimation inputs, sprint delivery, and risk mitigation activities. Build reusable assets such as data pipeline templates, model workflow patterns, notebooks, APIs, deployment scripts, validation utilities, and implementation playbooks. Support client discussions by explaining technical options, trade-offs, implementation constraints, and evidence for recommended AI/ML computational science approaches. Stay current with scientific AI, generative AI, agentic workflows, MLOps, digital twins, optimization, and cloud-native computational engineering patterns, and share learnings with the team. Required Qualifications