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:7 to 11 years
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? You will be part of the Technology for Operations team that acts as a trusted advisor and partner to Accenture Operations. The team provides innovative and secure technologies to help clients build an intelligent operating model, driving exceptional results. We work closely with the sales, offering and delivery teams to identify and build innovative solutions. The Tech For Operations (TFO) team provides innovative and secure technologies to help clients build an intelligent operating model, driving exceptional results. Works closely with the sales, offering and delivery teams to identify and build innovative solutions.,Tech for Operations involves using technology to improve and streamline business operations. This role includes implementing software solutions, automating processes, and managing IT systems that support operational activities. It also involves analyzing data to optimize performance and ensure efficiency. The goal is to enhance productivity and operational effectiveness through the effective use of technology. Major sub deals include AHO(Application Hosting Operations), ISMT (Infrastructure Management), Intelligent Automation Understanding of foundational principles and knowledge of Artificial Intelligence AI including concepts, techniques, and tools in order to use AI effectively.
What are we looking for? • CI/CD for ML – Implements robust CI/CD workflows for ML and LLM systems, enabling automated testing, versioning, and safe model releases across environments. • LLMOps – Operationalizes large language models with prompt/version management, RAG pipelines, evaluation frameworks, governance, and cost performance optimization. • Agentic Frameworks – Builds and manages agent based AI workflows with tool orchestration, reasoning chains, memory handling, and controlled autonomy. • Model Deployment – Deploys ML/LLM models as scalable APIs or batch services with version control, rollback strategies, and production grade reliability. • Data & Model Drift Monitoring – Establishes continuous monitoring to detect data, concept, and performance drift with alerting and retraining triggers. • Docker – Creates optimized, secure Docker images for ML workloads ensuring consistency across development, testing, and production. • Containerization – Applies containerization best practices to package ML services, dependencies, and runtime environments for portability and isolation. • Cloud Platforms – Leverages cloud platforms to provision, manage, and optimize ML/LLM infrastructure, storage, and deployment workflows. • Scalability – Designs sy • End to End ML/LLM Lifecycle Management Own the complete lifecycle of ML and LLM solutions from experimentation to production, including versioning, packaging, deployment, and retraining. • ML Pipeline Automation Design, build, and maintain scalable, automated pipelines for data ingestion, feature processing, model training, validation, and promotion. • CI/CD for ML & LLM Systems Implement CI/CD pipelines tailored for ML and LLM workflows, automating testing, validation, deployment, and rollback across environments. • LLMOps & RAG Enablement Operationalize LLM/SLM solutions including deployment, fine tuning, and retrieval augmented generation (RAG) pipelines with governance and evaluation controls. • Agentic Workflow Orchestration Build and manage pipelines for agent based and multi agent AI systems, handling reasoning, planning, tool execution, and controlled autonomy. • Model Deployment & Serving Deploy models as secure, scalable batch and real time services using containerized architectures and cloud platforms.
Roles and Responsibilities: •In this role you are required to do analysis and solving of moderately complex problems • May create new solutions, leveraging and, where needed, adapting existing methods and procedures • The person would require understanding of the strategic direction set by senior management as it relates to team goals • Primary upward interaction is with direct supervisor • May interact with peers and/or management levels at a client and/or within Accenture • Guidance would be provided when determining methods and procedures on new assignments • Decisions made by you will often impact the team in which they reside • Individual would manage small teams and/or work efforts (if in an individual contributor role) at a client or within Accenture • Please note that this role may require you to work in rotational shifts