Project Role: AI Infrastructure ArchitectProject Role Description Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost, and scalability of the 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

Experience Requirement

- Minimum 7.5 years of experience is required.

Educational Qualification

- 15 years of full time education.

AI Powered Tech TalentRole 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 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, 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.

Locations

- Job No. ATCI-5743451-S2067583 | Mumbai | Required Skill: AWS Machine Learning

📌 AI Infrastructure Architect (India)
🏢 Accenture
📍 India