AWS Machine Learning

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 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
Senior Engineer role in AI/ML Computational Science focused on designing building and integrating scalable scientific AI simulation intelligence computational modeling optimization and ML-enabled engineering solutions on Amazon Web Services (AWS).
You are expected to lead a technical workstream guide implementation choices mentor engineers contribute to solution design and support delivery leadership within a larger program.
The role converts computational science and engineering problems into practical AI/ML components scientific data pipelines model workflows and reusable cloud-native patterns that support scalable client outcomes.
Key Responsibilities
Lead the design and build of AI/ML computational science components that support scientific data ingestion simulation result processing feature engineering model development deployment and monitoring.
Translate scientific engineering and business problems into practical 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 solution 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 activities.
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.
Required Qualifications
Bachelors degree or equivalent in Computer Science Engineering Applied Mathematics Statistics Physics Computational Science Data Science or a related field.
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.
Required Skills/ Experience
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 for robust reusable maintainable engineering delivery.
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 with 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 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.
Good to Have Skills
Masters 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.