AI / ML Engineer

Overview

Senior AI/ML Engineer role in AI/ML computational science, designing, building and integrating scalable scientific AI, simulation intelligence, computational modeling, optimization and ML-enabled engineering solutions on Amazon Web Services (AWS). The role leads a technical workstream and mentors engineers within a larger program.

What you'll do

• Lead the design and build of AI/ML computational science components supporting scientific data ingestion, simulation result processing, feature engineering, model development, deployment and monitoring.
• Translate scientific, engineering and business problems into 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 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.
• 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.

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 7.5 years of experience is required.
• 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 program.
• Strong hands-on knowledge of AI/ML computational science workflows, scientific data processing, numerical modeling, optimization, simulation analytics, feature engineering and model deployment patterns.
• Strong Python, SQL, Git, testing, documentation, API, container and workflow orchestration skills.
• Practical 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.
• Working knowledge of MLOps, model governance, responsible AI, security, data privacy, observability, performance monitoring and production support practices.
• Ability to partner with domain experts and convert scientific concepts, equations, simulation outputs, experimental data and engineering constraints into buildable AI/ML solution components.
• Strong collaboration skills across engineering, research, product, client and delivery teams across multiple time zones.
• 2+ years of hands-on AWS experience across AI/ML development, scientific data pipelines, scalable compute, data engineering and secure cloud integration.
• Experience with AWS services such as SageMaker, Bedrock, Batch, EKS, ECS, Lambda, Step Functions, Glue, EMR, S3, FSx/Lustre, OpenSearch, IAM, VPC, CloudWatch and containerized deployment patterns.
• Ability to build AWS-based components for simulation data ingestion, surrogate modeling, optimization workflows, model training/inference, model monitoring and production deployment.
• Must have skill: AWS Machine Learning.
• 15 years full time education.

Nice to have

• Good to have skill: Data Science.
• 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, including technical discovery, implementation planning, solution demonstrations or delivery support.
• 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 with agentic AI workflows, RAG, vector search, knowledge graphs, semantic layers or scientific knowledge management.
• Experience creating reusable accelerators, implementation playbooks, solution design notes, proof-of-concept assets or technical enablement material.
• Cloud, data, AI/ML, MLOps or professional engineering certifications relevant to the selected platform.
• 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.

Details

• Location: Bengaluru.