À propos de ce poste AI / ML Engineer chez Accenture
Accenture · Sur site · Pune
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, Machine Learning (ML)
Minimum 18 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
Technical Architect role in AI/ML Computational Science focused on designing and leading enterprise-scale scientific AI, simulation intelligence, computational modeling, optimization, and ML-enabled engineering solutions on Amazon Web Services (AWS).
You are expected to own solution architecture, define engineering standards, shape complex programs, lead technical teams, and influence senior business and technology stakeholders.
The role translates complex scientific and engineering problems into scalable AI/ML, computational science, simulation, optimization, and data architecture solutions that can be reused across industries and client programs.
Key Responsibilities
Lead the end-to-end architecture for AI/ML computational science solutions, including scientific data ingestion, simulation data pipelines, feature engineering, model development, deployment, and monitoring.
Define technical direction for scientific AI, physics-informed ML, surrogate models, optimization algorithms, uncertainty quantification, generative AI for scientific workflows, and accelerated computing patterns.
Own architecture decisions across compute, storage, orchestration, MLOps, model governance, security, observability, performance, cost optimization, and integration with enterprise platforms.
Partner with client scientists, engineers, product owners, data architects, cloud engineers, and delivery leads to convert complex domain problems into practical AI/ML computational solutions.
Lead design reviews, architecture governance, technical risk assessment, solution estimation, implementation planning, and quality assurance for large and complex programs.
Guide engineering teams on reusable reference architectures, accelerators, coding standards, model lifecycle practices, and production readiness expectations.
Make and defend the business and technical case for computational science architectures with senior stakeholders, including value, feasibility, scalability, maintainability, and responsible AI considerations.
Support sales and pre-sales by shaping client solution narratives, technical proposals, demos, proofs of concept, and industry-specific AI/ML computational science offerings.
Drive thought leadership and asset development around scientific AI, simulation intelligence, digital twins, agentic workflows, generative AI, and cloud-native scientific computing.
Required Qualifications
Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
Minimum 8 years of experience in AI/ML, data science, computational science, scientific software engineering, simulation analytics, or quantitative engineering solutions.
Minimum 5 years of experience architecting and delivering enterprise-scale AI/ML, data, cloud, or high-performance computational platforms.
Minimum 4 years of experience with Python and scientific/ML frameworks such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, XGBoost, or similar libraries.
Minimum 3 years of experience with MLOps or production ML practices including experiment tracking, model registry, CI/CD, feature stores, testing, monitoring, and lifecycle governance.
Minimum 3 years of experience with scalable data engineering, distributed compute, workflow orchestration, APIs, batch/stream processing, and cloud-native deployment patterns.
Minimum 4 years of experience leading technical teams, reviewing architecture, guiding delivery, and communicating technical trade-offs to senior business and technology stakeholders.
Required Skills/ Experience
Strong architecture knowledge across AI/ML solution design, computational science workflows, numerical modeling, optimization, simulation data management, scientific data products, and model deployment patterns.
Hands-on experience with Python, SQL, Git, containers, APIs, orchestration tools, distributed processing, and engineering practices for robust, reusable, maintainable software.
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.
Practical knowledge of MLOps, model governance, responsible AI, security, data privacy, observability, model performance monitoring, and production support models.
Ability to work with domain experts and convert scientific concepts, equations, simulation outputs, experimental data, and engineering constraints into AI/ML design patterns.
Strong collaboration and stakeholder management skills with the ability to lead distributed teams across engineering, research, product, client, and delivery groups.
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.
5+ years of hands-on AWS experience across AI/ML architecture, scientific data platforms, scalable compute, data engineering, and enterprise integration.
Experience with AWS services such as SageMaker, Bedrock, Batch, EKS, ECS, Lambda, Step Functions, Glue, EMR, S3, FSx/Lustre, OpenSearch, Neptune, IAM, VPC, CloudWatch, and HPC/parallel compute patterns.
Ability to architect AWS-based workflows for simulation data pipelines, surrogate modeling, optimization loops, model training/inference, model monitoring, and secure deployment.
Good to Have Skills
Advanced degree such as 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 in architecture, advisory, delivery leadership, sales, or pre-sales roles.
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 creating reusable accelerators, reference architectures, implementation playbooks, technical whitepapers, or industry-specific solution assets.
Cloud, data, AI/ML, MLOps, or professional architecture certifications relevant to the selected platform.
Experience with agentic AI workflows, RAG, vector search, knowledge graphs, semantic layers, or scientific knowledge management.15 years full time education
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, Machine Learning (ML)
Minimum 18 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
Technical Architect role in AI/ML Computational Science focused on designing and leading enterprise-scale scientific AI, simulation intelligence, computational modeling, optimization, and ML-enabled engineering solutions on Amazon Web Services (AWS).
You are expected to own solution architecture, define engineering standards, shape complex programs, lead technical teams, and influence senior business and technology stakeholders.
The role translates complex scientific and engineering problems into scalable AI/ML, computational science, simulation, optimization, and data architecture solutions that can be reused across industries and client programs.
Key Responsibilities
Lead the end-to-end architecture for AI/ML computational science solutions, including scientific data ingestion, simulation data pipelines, feature engineering, model development, deployment, and monitoring.
Define technical direction for scientific AI, physics-informed ML, surrogate models, optimization algorithms, uncertainty quantification, generative AI for scientific workflows, and accelerated computing patterns.
Own architecture decisions across compute, storage, orchestration, MLOps, model governance, security, observability, performance, cost optimization, and integration with enterprise platforms.
Partner with client scientists, engineers, product owners, data architects, cloud engineers, and delivery leads to convert complex domain problems into practical AI/ML computational solutions.
Lead design reviews, architecture governance, technical risk assessment, solution estimation, implementation planning, and quality assurance for large and complex programs.
Guide engineering teams on reusable reference architectures, accelerators, coding standards, model lifecycle practices, and production readiness expectations.
Make and defend the business and technical case for computational science architectures with senior stakeholders, including value, feasibility, scalability, maintainability, and responsible AI considerations.
Support sales and pre-sales by shaping client solution narratives, technical proposals, demos, proofs of concept, and industry-specific AI/ML computational science offerings.
Drive thought leadership and asset development around scientific AI, simulation intelligence, digital twins, agentic workflows, generative AI, and cloud-native scientific computing.
Required Qualifications
Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
Minimum 8 years of experience in AI/ML, data science, computational science, scientific software engineering, simulation analytics, or quantitative engineering solutions.
Minimum 5 years of experience architecting and delivering enterprise-scale AI/ML, data, cloud, or high-performance computational platforms.
Minimum 4 years of experience with Python and scientific/ML frameworks such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, XGBoost, or similar libraries.
Minimum 3 years of experience with MLOps or production ML practices including experiment tracking, model registry, CI/CD, feature stores, testing, monitoring, and lifecycle governance.
Minimum 3 years of experience with scalable data engineering, distributed compute, workflow orchestration, APIs, batch/stream processing, and cloud-native deployment patterns.
Minimum 4 years of experience leading technical teams, reviewing architecture, guiding delivery, and communicating technical trade-offs to senior business and technology stakeholders.
Required Skills/ Experience
Strong architecture knowledge across AI/ML solution design, computational science workflows, numerical modeling, optimization, simulation data management, scientific data products, and model deployment patterns.
Hands-on experience with Python, SQL, Git, containers, APIs, orchestration tools, distributed processing, and engineering practices for robust, reusable, maintainable software.
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.
Practical knowledge of MLOps, model governance, responsible AI, security, data privacy, observability, model performance monitoring, and production support models.
Ability to work with domain experts and convert scientific concepts, equations, simulation outputs, experimental data, and engineering constraints into AI/ML design patterns.
Strong collaboration and stakeholder management skills with the ability to lead distributed teams across engineering, research, product, client, and delivery groups.
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.
5+ years of hands-on AWS experience across AI/ML architecture, scientific data platforms, scalable compute, data engineering, and enterprise integration.
Experience with AWS services such as SageMaker, Bedrock, Batch, EKS, ECS, Lambda, Step Functions, Glue, EMR, S3, FSx/Lustre, OpenSearch, Neptune, IAM, VPC, CloudWatch, and HPC/parallel compute patterns.
Ability to architect AWS-based workflows for simulation data pipelines, surrogate modeling, optimization loops, model training/inference, model monitoring, and secure deployment.
Good to Have Skills
Advanced degree such as 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 in architecture, advisory, delivery leadership, sales, or pre-sales roles.
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 creating reusable accelerators, reference architectures, implementation playbooks, technical whitepapers, or industry-specific solution assets.
Cloud, data, AI/ML, MLOps, or professional architecture certifications relevant to the selected platform.
Experience with agentic AI workflows, RAG, vector search, knowledge graphs, semantic layers, or scientific knowledge management.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.Visit us at www.accenture.com
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