20 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead end-to-end architecture for AI/ML computational science solutions spanning ingestion, simulation data 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, architects, product owners, cloud engineers, and delivery leads to turn complex domain problems into scalable 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, reusable accelerators, coding standards, model lifecycle practices, and production readiness.
- Communicate and defend technical and business cases to senior stakeholders, including value, feasibility, scalability, maintainability, and responsible AI considerations.
- Support sales and pre-sales through technical proposals, solution narratives, demos, proofs of concept, and industry-specific offerings.
- Drive thought leadership and reusable assets around scientific AI, digital twins, agentic workflows, generative AI, and cloud-native scientific computing.
Requirements
- Minimum 18 years of experience is required.
- Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
- At least 8 years of experience 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, and XGBoost.
- At least 3 years of MLOps or production ML experience, 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 computing, 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 technical trade-offs to senior stakeholders.
- Hands-on experience with Python, SQL, Git, containers, APIs, orchestration tools, distributed processing, and robust software engineering practices.
- Experience with computational-science 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 Databricks experience across lakehouse architecture, AI/ML workflows, scientific data engineering, distributed analytics, and enterprise integration.
- 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 or GPU acceleration experience, scientific or industrial domain expertise, reusable architecture asset development, relevant certifications, and experience with agentic AI, RAG, knowledge graphs, or 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.