Accenture Recruitment 2026 | Gurugram

Accenture Recruitment 2026:

Accenture is hiring a AI/ML Computational Science Analyst at Gurugram. The complete details about Accenture Recruitment 2026 as follows.

Job Role	AI/ML Computational Science Analyst
Qualification	B.E/B.Tech/BCA
Experience	Experienced
Job Location	Gurugram

Required Qualification :- B.E/B.Tech/BCA

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.

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.

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

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.

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.

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.

Apply In Below Link-

NOTE :- Only shortlisted candidates will receive the call letter for further rounds