20 hours ago
Mumbai, IndiaSenior
Responsibilities
- Lead the design and development of AI/ML computational science components for scientific data ingestion, simulation processing, feature engineering, model development, deployment, and monitoring.
- Translate scientific, engineering, and business problems into ML, optimization, surrogate modeling, simulation analytics, and data engineering solutions.
- Develop production-quality Python, SQL, APIs, workflow orchestration, cloud-native components, data pipelines, model workflows, notebooks, deployment scripts, and validation utilities.
- Collaborate with architects, data scientists, domain experts, cloud engineers, product owners, clients, and delivery leads to integrate solutions into broader enterprise architectures.
- Lead technical workstreams, guide implementation choices, contribute to design reviews and delivery planning, mentor junior engineers, and support risk mitigation.
- Support client discussions by explaining technical options, trade-offs, constraints, and recommended AI/ML computational science approaches.
- Develop AWS-based components for simulation ingestion, surrogate modeling, optimization, model training and inference, monitoring, and production deployment.
Requirements
- Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
- At least 7.5 years of overall experience is stated in the project requirements, with the role description also requiring at least 5 years in AI/ML, data science, computational science, scientific software engineering, simulation analytics, or quantitative engineering solutions.
- At least 3 years of experience designing AI/ML, data engineering, scientific computing, or cloud-native analytical solutions, including at least 3 years with Python and scientific or ML frameworks.
- At least 2 years of experience with MLOps or production ML practices, scalable data pipelines, distributed compute, APIs, workflow orchestration, containerized deployment, or technical workstream leadership.
- Strong hands-on experience with AI/ML computational science workflows, numerical modeling, optimization, simulation analytics, feature engineering, model deployment, Python, SQL, Git, testing, documentation, APIs, containers, and orchestration.
- At least 2 years of hands-on AWS experience across AI/ML development, scientific data pipelines, scalable compute, data engineering, and secure cloud integration.
- Experience with ML approaches including surrogate modeling, physics-informed ML, optimization, time series, anomaly detection, computer vision, NLP, generative AI, and uncertainty-aware modeling.
- Preferred qualifications include a master's degree or Ph.D., client-facing consulting, HPC, GPU acceleration, CUDA, MPI, distributed training, digital twins, scientific foundation models, agentic AI, RAG, vector search, knowledge graphs, and relevant certifications.
Categories
Data EngineeringML Engineering
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.