15 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Design and build AI/ML computational science components for scientific data ingestion, simulation result processing, feature engineering, model development, deployment, and monitoring.
- Translate scientific, engineering, and business problems into ML, optimization, surrogate modeling, simulation analytics, data engineering, and cloud solution patterns.
- Develop production-quality Python, SQL, APIs, workflow orchestration, and cloud-native components.
- Build reusable data pipeline templates, model workflow patterns, notebooks, APIs, deployment scripts, validation utilities, and implementation playbooks.
- Lead a technical workstream, guide implementation choices, mentor junior engineers, and support delivery leadership.
- Collaborate with architects, data scientists, domain experts, cloud engineers, product owners, clients, and delivery leads.
- Contribute to design reviews, technical decision logs, implementation plans, estimations, sprint delivery, risk mitigation, and production support.
- Explain technical options and trade-offs to clients and stay current with scientific AI, generative AI, agentic workflows, MLOps, digital twins, optimization, and cloud-native engineering patterns.
Requirements
- Bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field.
- Minimum 7.5 years of experience is required; the qualifications also specify at least 5 years in AI/ML or related computational disciplines.
- At least 3 years of experience designing and developing AI/ML, data engineering, scientific computing, or cloud-native analytical solutions.
- At least 3 years of experience 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, APIs, workflow orchestration, and containerized deployment.
- At least 2 years of hands-on AWS experience across AI/ML development, scientific data pipelines, scalable compute, data engineering, and secure cloud integration.
- 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, testing, documentation, Git, APIs, containers, and workflow orchestration.
- Experience with ML approaches including surrogate modeling, physics-informed ML, optimization, time series, anomaly detection, computer vision, NLP, generative AI, and uncertainty-aware modeling.
- Experience with AWS services including SageMaker, Bedrock, Batch, EKS, ECS, Lambda, Step Functions, Glue, EMR, S3, FSx/Lustre, OpenSearch, IAM, VPC, and CloudWatch.
- Preferred qualifications include a master's or Ph.D., external 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
Data EngineeringML Engineering
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.