AI / ML Engineer

Overview

AI-Powered Tech Talent Engineer role in AI/ML Computational Science, focused on hands-on development, integration, testing, and deployment of scientific AI, computational modeling, optimization, and ML-enabled engineering components on Amazon Web Services (AWS).

What you'll do

• Build AI/ML computational science components that support scientific data ingestion, preprocessing, simulation result handling, feature engineering, model training, inference, deployment, and monitoring.
• Design and implement assigned pieces of scientific AI, data engineering, optimization, surrogate modeling, simulation analytics, and ML workflow solutions.
• Write clean, tested, reusable Python, SQL, notebook, API, workflow orchestration, and cloud-native code aligned to project standards.
• Work with senior engineers, data scientists, domain experts, cloud engineers, architects, and delivery leads to ensure components integrate with the broader solution.
• Support data validation, model evaluation, experiment tracking, documentation, reproducibility, testing, debugging, and implementation handover activities.
• Create reusable technical assets such as notebooks, data preparation scripts, validation utilities, model training templates, API wrappers, deployment scripts, and documentation.
• Participate in design discussions and contribute technical observations on feasibility, implementation constraints, edge cases, and quality risks.
• Help assemble supporting evidence for recommended AI/ML computational science solutions, including experiment outputs, model metrics, data quality checks, and technical notes.

What you'll need

• Must have skill: AWS Machine Learning.
• Minimum 5 years of experience is required.
• 15 years full time education.
• Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
• Minimum 3 years of experience in AI/ML, data science, computational science, scientific software engineering, analytics engineering, or quantitative engineering solutions.
• Minimum 2 years of experience designing or developing AI/ML, data engineering, scientific computing, or cloud-native analytical solution components.
• Minimum 2 years of experience with Python and scientific/ML libraries such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, XGBoost, or similar libraries.
• Minimum 2 years of experience with SQL, data transformation, data validation, APIs, batch processing, notebook development, Git, and software engineering practices.
• Minimum 1 year of exposure to MLOps or ML lifecycle practices such as experiment tracking, model registry, CI/CD, testing, model monitoring, and model documentation.
• No leadership or commercial ownership requirement at this level.
• Hands-on knowledge of AI/ML computational science workflows, scientific data processing, feature engineering, numerical modeling basics, optimization concepts, simulation analytics, and model deployment support.
• Practical Python, SQL, Git, testing, documentation, notebook, API, container, and workflow orchestration skills for reliable engineering delivery.
• Experience with ML approaches relevant to computational science, including regression/classification, time series, anomaly detection, optimization, computer vision, NLP, generative AI, and surrogate modeling.
• Working knowledge of data quality, model evaluation, experiment tracking, responsible AI basics, security basics, observability basics, and production support expectations.
• Ability to work with domain experts and translate scientific data, simulation outputs, experimental data, and engineering constraints into buildable AI/ML tasks.
• Strong collaboration skills with the ability to work across engineering, research, product, client, and delivery teams across multiple time zones.
• 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.
• 2+ years of hands-on AWS experience across AI/ML development, data pipelines, scalable compute, storage, integration, and cloud fundamentals.
• Experience with AWS services such as SageMaker, Bedrock, Batch, Lambda, Step Functions, Glue, EMR, S3, OpenSearch, IAM, CloudWatch, EKS, ECS, and containerized deployment patterns.
• Ability to implement AWS-based components for simulation data ingestion, feature engineering, ML training/inference, workflow orchestration, and model monitoring support.

Nice to have

• Master's degree or advanced coursework in Computer Science, Computational Science, Applied Mathematics, Physics, Engineering, Operations Research, Statistics, or a related field.
• Experience working in a client-facing consulting delivery environment or contributing to technical discovery, demos, implementation notes, or delivery support material.
• Exposure to HPC, GPU acceleration, CUDA, MPI, distributed processing, workload schedulers, parallel compute, or cloud-based scale-out compute patterns.
• Exposure to digital twins, scientific foundation models, materials informatics, computational chemistry, bioinformatics, geospatial analytics, industrial optimization, or engineering simulation workflows.
• Exposure to agentic AI workflows, RAG, vector search, knowledge graphs, semantic layers, or scientific knowledge management.
• Experience creating reusable notebooks, scripts, proof-of-concept assets, implementation templates, or technical enablement material.
• Cloud, data, AI/ML, MLOps, or professional engineering certification relevant to the selected platform.
• Team lead exposure is good to have.

Details

• Location: Pune.