AI/ML Computational Science Analyst

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

Designation: AI/ML Computational Science Analyst

Qualifications:BE/BTech/BCA

Years of Experience:1 to 2 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 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 Engineer – 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)
• Good Python programming fundamentals including scripting, data structures and Object-Oriented Programming concepts.
• Basic exposure to AI/ML concepts, GenAI, prompt engineering or LLM-enabled applications.
• Understanding of REST APIs, JSON, Git and software development lifecycle basics.
• Ability to write clean code, test outputs, document work, and learn fast in a POD-based delivery model. 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 RAG, vector databases, LangChain, LangGraph, Semantic Kernel or CrewAI is preferred.
• Basic understanding of cloud platforms, Docker, CI/CD, testing and observability concepts.
• Interest in responsible AI, AI guardrails, enterprise integration and production-readiness practices. Roles and Responsibilities:
• Support development of Python-led AI/ML components, scripts and AI pipeline utilities under guidance from senior engineers.
• Assist in prompt engineering, structured output testing, basic RAG implementation, and validation of LLM responses.
• Participate in API testing, integration validation, documentation, and defect resolution activities.
• Contribute to unit testing, AI output checks, data preparation, debugging, and deployment support.
• Build foundational understanding of Agentic AI workflows, tool calling, orchestration and enterprise integration patterns.