AI Infrastructure Architect

Project Role : AI Infrastructure Architect Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration. Must have skills : AWS Machine Learning Good to have skills : Snowflake Data Warehouse Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education AI Powered Tech Talent Role Summary / Description 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). Role scope: As a Senior Engineer, you will 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.

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.

Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field. Minimum 5 years of experience in AI/ML, data science, computational science, scientific software engineering, simulation analytics, or quantitative engineering solutions. Minimum 3 years of experience designing and developing AI/ML, data engineering, scientific computing, or cloud-native analytical solutions. Minimum 3 years of experience with Python and scientific/ML frameworks such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, XGBoost, or similar libraries. Minimum 2 years of experience with MLOps or production ML practices including experiment tracking, model registry, CI/CD, testing, monitoring, and lifecycle governance. Minimum 2 years of experience with scalable data pipelines, distributed compute, batch/stream processing, APIs, workflow orchestration, and containerized deployment patterns. Minimum 2 years of experience leading a technical workstream, mentoring engineers, or guiding implementation within a larger