18 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, model governance, security, observability, performance, cost optimization, and enterprise integration.
- Partner with scientists, engineers, product owners, data architects, cloud engineers, and delivery leads to translate domain problems into scalable solutions.
- Lead design reviews, architecture governance, technical risk assessment, estimation, implementation planning, and quality assurance for complex programs.
- Guide engineering teams on reference architectures, coding standards, model lifecycle practices, accelerators, and production readiness.
- Present and defend technical and business cases to senior stakeholders, including considerations for value, feasibility, scalability, maintainability, and responsible AI.
- Support sales and pre-sales through solution narratives, technical proposals, demos, proofs of concept, and industry-specific offerings.
- Develop thought leadership and reusable assets for scientific AI, simulation intelligence, digital twins, agentic workflows, generative AI, and cloud-native scientific computing.
Requirements
- Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
- A minimum of 12 years of experience is required; the 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.
- 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 experience with MLOps or production ML practices, including experiment tracking, model registries, CI/CD, feature stores, testing, monitoring, and lifecycle governance.
- At least 3 years of experience with scalable data engineering, distributed compute, workflow orchestration, APIs, batch or stream processing, and cloud-native deployment patterns.
- At least 4 years of experience leading technical teams, reviewing architecture, guiding delivery, and communicating trade-offs to senior stakeholders.
- Strong knowledge of AI/ML solution design, computational science workflows, numerical modeling, optimization, simulation data management, scientific data products, and model deployment.
- At least 5 years of hands-on Databricks experience across Lakehouse architecture, AI/ML workflows, scientific data engineering, distributed analytics, and enterprise integration.
- Experience with Python, SQL, Git, containers, APIs, orchestration tools, distributed processing, MLOps, model governance, responsible AI, security, privacy, observability, and production support.
- Experience applying Databricks-enabled AI/ML computational science and Lakehouse solutions in relevant industries.
- Experience with Databricks Lakehouse, Delta Lake, Unity Catalog, Workflows, notebooks, jobs, MLflow, Feature Store, Mosaic AI, vector search, Spark, and AWS, Azure, or GCP integrations.
- 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, knowledge graphs, and semantic layers.
Tech Stack
Apache SparkAWSAzureDatabricksGitGoogle Cloud PlatformMLflowNumPyPandasPythonPyTorchscikit-learnSciPySQLTensorFlowXGBoost
Categories
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.