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 : NA Minimum 15 year(s) of experience is required Educational Qualification : 15 years full time education Role Summary / Description AI Powered Tech Talent Technical Architect role in AI/ML Computational Science focused on designing and leading enterprise-scale scientific AI, simulation intelligence, computational modeling, optimization, and ML-enabled engineering solutions on Amazon Web Services (AWS). You are expected to own solution architecture, define engineering standards, shape complex programs, lead technical teams, and influence senior business and technology stakeholders. The role translates complex scientific and engineering problems into scalable AI/ML, computational science, simulation, optimization, and data architecture solutions that can be reused across industries and client programs. Key Responsibilities Lead the end-to-end architecture for AI/ML computational science solutions, including scientific data ingestion, simulation data pipelines, feature engineering, model development, deployment, and monitoring. Define technical direction for scientific AI, physics-informed ML, surrogate models, optimization algorithms, uncertainty quantification, generative AI for scientific workflows, and accelerated computing patterns. Own architecture decisions across compute, storage, orchestration, MLOps, model governance, security, observability, performance, cost optimization, and integration with enterprise platforms. Partner with client scientists, engineers, product owners, data architects, cloud engineers, and delivery leads to convert complex domain problems into practical AI/ML computational solutions. Lead design reviews, architecture governance, technical risk assessment, solution estimation, implementation planning, and quality assurance for large and complex programs. Guide engineering teams on reusable reference architectures, accelerators, coding standards, model lifecycle practices, and production readiness expectations. Make and defend the business and technical case for computational science architectures with senior stakeholders, including value, feasibility, scalability, maintainability, and responsible AI considerations. Support sales and pre-sales by shaping client solution narratives, technical proposals, demos, proofs of concept, and industry-specific AI/ML computational science offerings. Drive thought leadership and asset development around scientific AI, simulation intelligence, digital twins, agentic workflows, generative AI, and cloud-native scientific computing. Required Qualifications