20 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Architect and build custom AI infrastructure and hardware solutions while optimizing performance, power consumption, cost, and scalability.
- Lead the design and implementation of AI/ML computational science components for data ingestion, simulation processing, feature engineering, model development, deployment, and monitoring.
- Develop production-quality Python, SQL, APIs, workflow orchestration, cloud-native components, reusable data pipeline templates, model workflows, notebooks, deployment scripts, and validation utilities.
- Translate scientific, engineering, and business problems into ML, optimization, surrogate modeling, simulation analytics, and data engineering solution patterns.
- Integrate solution components with technical architects, data scientists, domain experts, cloud engineers, product owners, and delivery leads.
- Lead technical workstreams, guide implementation choices, mentor engineers, and contribute to design reviews, technical decisions, estimates, delivery planning, and risk mitigation.
- Support client discussions by explaining technical options, trade-offs, constraints, and recommended AI/ML computational science approaches.
- Stay current with scientific AI, generative AI, agentic workflows, MLOps, digital twins, optimization, and cloud-native computational engineering patterns.
Requirements
- Minimum 12 years of experience is required for the project role; the detailed qualifications also require at least 5 years in AI/ML, data science, computational science, scientific software engineering, simulation analytics, or quantitative engineering solutions.
- Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
- At least 3 years of experience designing AI/ML, data engineering, scientific computing, or cloud-native analytical solutions, including 3 years with Python and scientific or ML frameworks such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, or XGBoost.
- At least 2 years of experience with MLOps or production ML practices, scalable data pipelines, distributed compute, batch or stream processing, APIs, workflow orchestration, and containerized deployment.
- At least 2 years of experience leading a technical workstream, mentoring engineers, or guiding implementation within a larger program.
- Strong knowledge of scientific data processing, numerical modeling, optimization, simulation analytics, feature engineering, model deployment, Python, SQL, Git, testing, documentation, APIs, containers, and workflow orchestration.
- Experience with AWS-enabled AI/ML computational science solutions and at least 2 years of hands-on AWS experience across AI/ML development, scientific data pipelines, scalable compute, data engineering, and secure cloud integration.
- Working knowledge of MLOps, model governance, responsible AI, security, data privacy, observability, performance monitoring, and production support.
- Preferred qualifications include a master's degree or PhD, client-facing consulting, HPC, GPU acceleration, CUDA, MPI, distributed training, workload schedulers, digital twins, scientific foundation models, agentic AI, RAG, vector search, knowledge graphs, and relevant certifications.
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.