AWS Engineer

Senior Engineer role in AI/ML Computational Science focused on designing, building, and integrating scalable scientific AI, simulation intelligence, computational modelling, optimisation, 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.

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, optimisation, surrogate modelling, 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, optimisation, and cloud-native computational engineering patterns, and share learnings with the team.

Requirements:

• Bachelor's 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 containerised 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 modelling, optimisation, 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 modelling, physics-informed ML, optimisation, time series, anomaly detection, computer vision, NLP, generative AI, and uncertainty-aware modelling.
• 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 containerised deployment patterns.
• Ability to build AWS-based components for simulation data ingestion, surrogate modelling, optimisation workflows, model training/inference, model monitoring, and production deployment.

Good to Have Skills:

• Master's or PhD 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 optimisation, 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.
• 15 years of full-time education.