AI/ML Computational Science Specialist

What would you do? As a part of the Knowledge-AI team, you will lead the development and deployment of AI-based assets using the latest technologies and advanced AI techniques. You will be responsible for building scalable, production-ready AI solutions using Machine Learning, Deep Learning, NLP, and Generative AI (GenAI) approaches. You will apply your expertise to understand complex business processes and design AI-driven solutions that reduce turnaround time and improve efficiency. You will collaborate with business experts, researchers, and platform engineers to deliver enterprise-grade AI solutions that align with Accenture’s core principles and deliver measurable outcomes. You will also contribute to innovation through patents, reusable assets, and enhancements to existing solutions. ________________________________________
What are we looking for? • Strong programming skills in Python and familiarity with libraries such as NumPy, pandas, scikit-learn, Matplotlib, and Seaborn • Experience with Machine Learning and Deep Learning frameworks (e.g., TensorFlow, PyTorch, Keras) • Hands-on experience with Natural Language Processing (NLP) and Large Language Models (LLMs) • Exposure to Generative AI, Prompt Engineering, and orchestration frameworks (e.g., LangChain, LlamaIndex) • Experience with deploying AI models using FastAPI, Flask, or gRPC • Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes) • Experience with cloud platforms (AWS, Azure, GCP) and services like SageMaker, Vertex AI, Azure ML • Understanding of MLOps tools and practices (e.g., MLflow, DVC, Kubeflow, CI/CD pipelines) • Ability to perform under pressure and manage multiple stakeholders • Strong business acumen and ability to translate business challenges into AI solutions • Excellent collaboration and communication skills across cross-functional teams ________________________________________ • Designing scalable and modular AI architectures • Implementing CI/CD pipelines for ML workflows using tools like GitHub Actions, Jenkins, or Azure DevOps • Working with vector databases (e.g., Pinecone, FAISS) for semantic search and RAG pipelines • Applying model optimization techniques (e.g., ONNX, TensorRT, quantization, distillation) • Implementing observability and monitoring using Prometheus, Grafana, Arize AI, or WhyLabs • Understanding of responsible AI practices (e.g., fairness, explainability, bias detection) • Agile methodologies and sprint planning • Good understanding of SQL and NoSQL databases
Roles and Responsibilities: •• Design, develop, and deploy AI-based solutions across multiple domains • Provide technical leadership and mentorship to junior team members • Industrialize and commercialize AI products and solutions • Analyze complex business processes and propose AI-based optimizations • Manage cross-functional stakeholders and ensure alignment with business goals • Drive innovation and contribute to patents and reusable assets • Lead product/solution development and collaborate with clients to deliver value • Operate in an Agile development environment and proactively manage risks and blockers • Ensure adherence to quality control best practices including unit, integration, UAT, and performance testing • Maintain comprehensive documentation and deliver compelling storytelling and presentations to stakeholders