AI / ML Engineer

Overview

Technical Architect role in AI/ML Computational Science, designing and leading enterprise-scale scientific AI, simulation intelligence, computational modeling, optimization, and ML-enabled engineering solutions on AWS.

What you'll do

• Lead 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.

What you'll need

• 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, distributed compute, workflow orchestration, APIs, batch/stream processing, and cloud-native deployment patterns.
• Minimum 4 years of experience leading technical teams, reviewing architecture, guiding delivery, and communicating technical trade-offs to senior business and technology stakeholders.
• Strong architecture knowledge across AI/ML solution design, computational science workflows, numerical modeling, optimization, simulation data management, scientific data products, and model deployment patterns.
• Hands-on experience with Python, SQL, Git, containers, APIs, orchestration tools, distributed processing, and engineering practices for robust, reusable, maintainable software.
• Experience with ML approaches relevant to computational science, including surrogate modeling, physics-informed ML, optimization, time series, anomaly detection, computer vision, NLP, generative AI, and uncertainty-aware modeling.
• Practical knowledge of MLOps, model governance, responsible AI, security, data privacy, observability, model performance monitoring, and production support models.
• Ability to work with domain experts and convert scientific concepts, equations, simulation outputs, experimental data, and engineering constraints into AI/ML design patterns.
• Strong collaboration and stakeholder management skills with the ability to lead distributed teams across engineering, research, product, client, and delivery groups.
• Industry experience applying AWS-enabled AI/ML computational science solutions in domains such as life sciences, healthcare, energy, utilities, manufacturing, chemicals, materials, aerospace, automotive, financial services, or public sector research.
• 5+ years of hands-on AWS experience across AI/ML architecture, scientific data platforms, scalable compute, data engineering, and enterprise integration.
• Experience with AWS services such as SageMaker, Bedrock, Batch, EKS, ECS, Lambda, Step Functions, Glue, EMR, S3, FSx/Lustre, OpenSearch, Neptune, IAM, VPC, CloudWatch, and HPC/parallel compute patterns.
• Ability to architect AWS-based workflows for simulation data pipelines, surrogate modeling, optimization loops, model training/inference, model monitoring, and secure deployment.
• 15 years full time education.

Nice to have

• Advanced degree such as Master's or Ph.D. in Computer Science, Computational Science, Applied Mathematics, Physics, Engineering, Operations Research, Statistics, or a related field.
• External client-facing consulting experience in architecture, advisory, delivery leadership, sales, or pre-sales roles.
• Experience with HPC, GPU acceleration, CUDA, MPI, distributed training, workload schedulers, or cloud-based parallel compute patterns.
• Experience with digital twins, scientific foundation models, materials informatics, computational chemistry, bioinformatics, geospatial analytics, industrial optimization, or engineering simulation workflows.
• Experience creating reusable accelerators, reference architectures, implementation playbooks, technical whitepapers, or industry-specific solution assets.
• Cloud, data, AI/ML, MLOps, or professional architecture certifications relevant to the selected platform.
• Experience with agentic AI workflows, RAG, vector search, knowledge graphs, semantic layers, or scientific knowledge management.

Details

• Location: Bengaluru.