3 days ago
Pune, IndiaStaff+
Responsibilities
- Lead end-to-end architecture for AI/ML computational science solutions spanning 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.
- Translate scientific and engineering problems into scalable, reusable AI/ML, computational science, simulation, optimization, and data architecture solutions.
- Lead design reviews, architecture governance, technical risk assessment, solution 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, demonstrations, and proofs of concept.
- Drive thought leadership and reusable assets for scientific AI, simulation intelligence, digital twins, agentic workflows, generative AI, and cloud-native scientific computing.
Requirements
- At least 18 years of professional experience is required; the role also specifies minimum experience thresholds of 8 years in AI/ML or related fields, 5 years architecting enterprise-scale platforms, and 4 years leading technical teams.
- Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field, with 15 years of full-time education.
- 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.
- At least 3 years of MLOps or production ML experience covering experiment tracking, model registries, CI/CD, feature stores, testing, monitoring, and lifecycle governance.
- At least 3 years of experience with scalable data engineering, distributed computing, workflow orchestration, APIs, batch or stream processing, and cloud-native deployment.
- Hands-on experience with Python, SQL, Git, containers, APIs, orchestration tools, distributed processing, and maintainable software engineering practices.
- Experience with scientific ML approaches including surrogate modeling, physics-informed ML, optimization, time series, anomaly detection, computer vision, NLP, generative AI, and uncertainty-aware modeling.
- At least 5 years of hands-on AWS experience across AI/ML architecture, scientific data platforms, scalable compute, data engineering, and enterprise integration.
- Experience architecting AWS workflows for simulation pipelines, surrogate modeling, optimization, training and inference, monitoring, and secure deployment.
- Strong knowledge of model governance, responsible AI, security, data privacy, observability, model performance monitoring, and production support.
- Preferred qualifications include a master's degree or Ph.D., client-facing consulting or pre-sales experience, HPC and GPU acceleration, CUDA, MPI, digital twins, scientific foundation models, reusable architecture assets, relevant certifications, and agentic AI, RAG, vector search, or knowledge graph experience.
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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.