AI/ML Computational Science Sr Analyst

AI/ML Computational Science Sr Analyst

Skill required: Tech for Operations - Artificial Intelligence (AI)
Designation: AI/ML Computational Science Sr Analyst
Qualifications:BE/BTech/BCA
Years of Experience:3 to 5 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? Reinvention Deployment Engineering (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 agents handle scale, speed, and data-driven execution The RDE- AI/ML Computational Science Sr Analyst – 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 AI/ML 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, rather than narrow single-skill specialization Must Have Skills • Python & Full-Stack Development • Agentic AI (LangChain, LangGraph, MCP, RAG) • Strong Python programming experience including async programming, OOP, data structures and scripting. • Hands-on exposure to GenAI / LLM solutioning, prompt engineering, structured outputs and RAG implementation. • Experience developing REST APIs, microservices or integration components. • Working knowledge of Git, CI/CD, Docker and cloud deployment concepts. • Ability to independently own modules and collaborate effectively in POD-based delivery.
What are we looking for? •Skill Area •Agentic AI Concepts - Deep understanding of AI agent design, reasoning loops, orchestration patterns & multi-agent coordination architectures. Core differentiator; senior levels lead architecture design •Agentic AI Concepts - Tool calling, function routing, agent memory & state management, autonomous decision-making patterns. Applicable across levels; depth scales with seniority •LLM & Prompt Engineering - Hands-on with LLMs (GPT-4, Claude, Gemini); prompt engineering, few-shot, chain-of-thought & structured output techniques. Focus on prompt craft •LLM & Prompt Engineering - RAG pipeline design, vector database integration (Pinecone, Weaviate, ChromaDB) & semantic search for enterprise grounding. RAG critical for enterprise-grade AI accuracy •AI Frameworks - Exposure in LangGraph, LangChain, Semantic Kernel or CrewAI for production-grade agentic workflow development. •Programming & APIs - Strong Python skills — async programming, OOP, data structures & scripting for AI pipelines; Java/.NET acceptable. Python strongly preferred for AI workloads •Programming & APIs - REST/GraphQL API development, microservices design & enterprise application integration patterns - Integration skills essential for enterprise deployment •Skill Area •Cloud & DevOps - Azure / AWS / GCP hands-on experience; cloud-native architecture, infrastructure provisioning & managed AI services. AWS preferred for this engagement; cloud-agnostic skills valued •Cloud & DevOps - Containerization (Docker, Kubernetes), CI/CD pipeline setup, GitOps & automated deployment practices. CI/CD mandatory •Security & Responsible AI - Security principles, identity management (OAuth, Azure AD), AI guardrails, bias mitigation & enterprise compliance •Enterprise Integration - Integrating with enterprise platforms: ServiceNow, Appian, SAP, Salesforce & Microsoft ecosystem (M365, Teams, Power Platform). Platform experience maps directly to client landscape •Testing & Observability - AI solution testing, LLM output evaluation, observability (tracing, monitoring), performance tuning & cost optimization. Observability critical for production AI agents Secondary Skills • Exposure to LangGraph, LangChain, Semantic Kernel, CrewAI or similar AI frameworks. • Understanding of vector databases, semantic search, AI testing and monitoring concepts. • Exposure to Azure, AWS or GCP, Kubernetes and enterprise integration platforms is preferred.
Roles and Responsibilities: • Independently develop Python-based AI/ML modules, reusable scripts, APIs and integration components. • Build and test GenAI components including prompts, structured outputs, RAG pipelines and vector search integrations. • Support agentic workflows involving tool calling, orchestration, state handling and function routing. • Participate in CI/CD, Docker-based packaging, deployment support and cloud-native engineering activities. • Own assigned build-test-deploy tasks and collaborate with L9/L8 engineers on production readiness.