3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead end-to-end architecture for AI/ML computational science solutions, including scientific data ingestion, simulation pipelines, feature engineering, model development, deployment, and monitoring.
- Define technical direction for scientific AI, physics-informed ML, surrogate models, optimization, uncertainty quantification, generative AI, and accelerated computing.
- Own architecture decisions across compute, storage, orchestration, MLOps, governance, security, observability, performance, cost optimization, and enterprise integration.
- Partner with scientists, engineers, product owners, data architects, cloud engineers, delivery leads, and clients to convert complex problems into practical AI/ML solutions.
- Lead design reviews, architecture governance, technical risk assessment, estimation, implementation planning, and quality assurance for complex programs.
- Guide engineering teams on reference architectures, accelerators, coding standards, model lifecycle practices, and production readiness.
- Present and defend technical and business cases to senior stakeholders and support sales and pre-sales through proposals, demos, proofs of concept, and solution narratives.
- Drive thought leadership and reusable assets related to scientific AI, simulation intelligence, digital twins, agentic workflows, generative AI, and cloud-native scientific computing.
Requirements
- A bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field is required.
- At least 15 years of overall experience is stated for the project role; required qualifications also specify at least 8 years in AI/ML, data science, computational science, scientific software engineering, simulation analytics, or quantitative engineering solutions.
- At least 5 years of experience architecting and delivering enterprise-scale AI/ML, data, cloud, or high-performance computational platforms is required.
- At least 4 years of experience with Python and scientific or ML frameworks such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, or XGBoost is required.
- At least 3 years of MLOps or production ML experience, including experiment tracking, model registries, CI/CD, feature stores, testing, monitoring, and lifecycle governance, is required.
- At least 3 years of experience with scalable data engineering, distributed computing, workflow orchestration, APIs, batch or stream processing, and cloud-native deployment is required.
- At least 4 years of experience leading technical teams, reviewing architecture, guiding delivery, and communicating trade-offs to senior stakeholders is required.
- Strong knowledge of AI/ML solution design, computational science workflows, numerical modeling, optimization, simulation data, model deployment, responsible AI, security, privacy, observability, and production support is required.
- Hands-on experience with Python, SQL, Git, containers, APIs, orchestration tools, distributed processing, and robust software engineering practices is required.
- At least 5 years of hands-on AWS experience across AI/ML architecture, scientific data platforms, scalable compute, data engineering, and enterprise integration is required.
- Experience applying AWS-enabled AI/ML computational science solutions in relevant industries and working with scientific or engineering domain experts is required.
- Preferred qualifications include an advanced degree, client-facing consulting or pre-sales experience, HPC and GPU acceleration, CUDA, MPI, distributed training, digital twins, scientific foundation models, reusable architecture assets, relevant certifications, agentic AI, RAG, vector search, knowledge graphs, and semantic layers.
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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.