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 : Machine Learning (ML) Good to have skills : Databricks Unified Data Analytics Platform, Microsoft Azure Data Services Minimum 18 year(s) of experience is required Educational Qualification : 15 years full time education Summary: AI Powered Tech Talent Role Summary / Description 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 Databricks Lakehouse. Role scope: As a Technical Architect, you will 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.

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.

Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field. Minimum 8 years of experience in AI/ML, data science, computational science, scientific software engineering, simulation analytics, or quantitative engineering solutions. Minimum 5 years of experience architecting and delivering enterprise-scale AI/ML, data, cloud, or high-performance computational platforms. Minimum 4 years of experience with Python and scientific/ML frameworks such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, XGBoost, or similar libraries. Minimum 3 years of experience with MLOps or production ML practices including experiment tracking, model registry, CI/CD, feature stores, testing, monitoring, and lifecycle governance. Minimum 3 years of experience with scalable data engineering,