AI ML Computational Science Specialist

Skill required: Tech for Operations - Artificial Intelligence (AI)

Designation: AI/ML Computational Science Specialist

Qualifications:Any Graduation

Years of Experience:5 – 7 years

Language - Ability:English(International) - Intermediate

About Accenture

Accenture is a global professional services company with leading capabilities in digital, cloud and security.Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Technology and Operations services, and Accenture Song— all powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. Our 784,000 people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities.Visit us at www.accenture.com

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