19 hours ago
Pune, IndiaSenior
Responsibilities
- Build AI/ML computational science components for scientific data ingestion, preprocessing, simulation result handling, feature engineering, model training, inference, deployment, and monitoring.
- Design and implement components for data engineering, optimization, surrogate modeling, simulation analytics, and ML workflows.
- Write tested, reusable Python, SQL, notebook, API, workflow orchestration, and cloud-native code.
- Create reusable notebooks, data preparation scripts, validation utilities, model training templates, API wrappers, deployment scripts, and documentation.
- Support data validation, model evaluation, experiment tracking, reproducibility, testing, debugging, implementation handover, and technical evidence preparation.
- Collaborate with senior engineers, data scientists, domain experts, cloud engineers, architects, and delivery teams.
- Implement AWS-based components for simulation data ingestion, feature engineering, ML training and inference, workflow orchestration, and model monitoring.
Requirements
- A bachelor's degree or equivalent in Computer Science, Engineering, Applied Mathematics, Statistics, Physics, Computational Science, Data Science, or a related field is required.
- The posting states a minimum of 5 years of experience, while the required qualifications also specify at least 3 years in AI/ML, data science, computational science, scientific software engineering, analytics engineering, or quantitative engineering solutions.
- At least 2 years of experience designing or developing AI/ML, data engineering, scientific computing, or cloud-native analytical solution components is required.
- At least 2 years of experience with Python and scientific or ML libraries such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, JAX, or XGBoost is required.
- At least 2 years of experience with SQL, data transformation, data validation, APIs, batch processing, notebooks, Git, and software engineering practices is required.
- At least 1 year of exposure to MLOps or ML lifecycle practices such as experiment tracking, model registries, testing, model monitoring, and model documentation is required.
- At least 2 years of hands-on AWS experience across AI/ML development, data pipelines, scalable compute, storage, integration, and cloud fundamentals is required.
- Experience with AWS services including SageMaker, Bedrock, Batch, Lambda, Step Functions, Glue, EMR, S3, OpenSearch, IAM, CloudWatch, EKS, ECS, and containerized deployment patterns is required.
- Knowledge of computational science workflows, scientific data processing, numerical modeling, optimization, simulation analytics, model deployment, data quality, model evaluation, responsible AI, security, observability, and production support is required.
- Experience with ML approaches including regression, classification, time series, anomaly detection, optimization, computer vision, NLP, generative AI, and surrogate modeling is required.
- Preferred qualifications include a master's degree or advanced coursework, client-facing consulting delivery experience, HPC or GPU experience, scientific and engineering simulation workflows, agentic AI, RAG, vector search, knowledge graphs, reusable technical assets, and relevant cloud, data, AI/ML, MLOps, or engineering certifications.
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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.