AI/ML Computational Science Assoc Mgr

AI/ML Computational Science Assoc Mgr

Skill required: Tech for Operations - Artificial Intelligence (AI)
Designation: RDE AIML Computational Science Assoc Mgr
Qualifications:BE/BTech
Years of Experience:8 to 12 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? • Reinvention Deployed Engineer (RDE) is an approach used by Accenture to reimagine and accelerate the delivery of engineering and R&D solutions. It focuses on combining human expertise with AI-driven agents to enhance innovation velocity, streamline processes, and reduce time to market for products and services. RDE emphasizes a value-driven mindset, where humans define objectives and ethical guardrails while AI agent • handle scale, speed, and data-driven execution. The RDE AIML Computational Science Assoc Manager Agentic AI Integration will design, build, integrate, test and deploy AI native and Agentic AI solutions for Accenture Operations RDE PODs. The role is intended for multi skilled engineers with Python and AIML as the primary capability, supported by working knowledge across integration, cloud, DevOps, testing • observability, responsible AI and enterprise platforms The role supports the RDE POD model where engineers are expected to operate close to client problems, contribute across the delivery lifecycle, reduce handoffs, and accelerate client-facing outcomes through compact, T shaped teams. The hiring approach should therefore prioritize strong primary skill depth plus adjacent skill breadth
What are we looking for? • Python, Full Stack Development, Agentic AI LangChain, LangGraph, MCP, RAG,. Strong Python engineering experience including async programming, Object Oriented Programming, data structures, scripting and production grade AI pipeline development. Hand on experience with GenAI LLM solution development, prompt engineering, structured outputs, RAG pipelines, vector databases and semantic search.Practical understanding of Agentic AI design patterns including orchestration, tool use, function calling, state management, memory and multi-agent coordination.
Roles and Responsibilities: • Lead design and development of Python led Agentic AI solutions, including agent workflows, orchestration, tool calling, function routing, memory state management and multi agent coordination. Define and implement LLM enabled solution patterns using prompt engineering, structured outputs, RAG, vector databases, semantic search and enterprise grounding approaches. Evaluate and recommend agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel and CrewAI based on use case complexity, maintainability, scalability and enterprise readiness. Own integration design across APIs, microservices and enterprise platforms such as ServiceNow, Appian, SAP, Salesforce, Microsoft 365, Teams and Power Platform.Drive cloud and DevOps readiness using Azure, AWS or GCP, including Docker, Kubernetes, CI CD, GitOps, infrastructure automation and deployment governance.Establish testing, observability and LLM evaluation practices covering AI output quality, tracing, monitoring, performance, cost optimization and production support readiness. Embed responsible AI, security, identity, compliance and guardrail requirements into solution design, including OAuth, Azure AD, data protection and enterprise risk controls. Coach junior engineers, review designs and code, unblock technical delivery, and enable RDE POD members to operate as T shaped, multi skilled contributors.