AI Infrastructure Architect

Project Role : AI Infrastructure Architect
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : AWS Machine Learning
Good to have skills : Snowflake Data Warehouse
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

AI Powered Tech Talent
Role Summary / Description
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).
Role scope: As a Senior Engineer, you will 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
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 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
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.
15 years full time education

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

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