Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : AWS Machine Learning
Good to have skills : Data Science
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
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).
You are expected to be a strong hands-on individual contributor who delivers assigned solution components, collaborates with project teams, and grows into broader technical ownership over time.
The role supports computational science programs by building practical AI/ML components, scientific data workflows, reusable code assets, model pipelines, and cloud-native integrations under guidance from senior engineers and architects.
Key Responsibilities
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 clearly 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.
Keep developing skills in scientific AI, generative AI, agentic workflows, digital twins, MLOps, optimization, and cloud-native computational engineering.
Required Qualifications
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 team lead exposure is good to have.
Required Skills/ Experience
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.
Good to Have Skills
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.
15 years full time education
