Accenture in India
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Gurugram · On-site · 1-4 Yrs · Sep 25
Business consulting · 10000+ Employees · Dublin, Ireland
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. Visit us at accenture.com.
Hiring: Engineering
What employees say
+ Good exposure and learning is great
− Nothing as such, I find the org good culture perspective
More reviews on the company profile.
Interview experience · Quality Engineer
The interview process was mainly focused on academics and my professional background, including questions about my graduation, coursework, and previous internship experience. The interviewer also asked several situationa
Easy difficulty · Indore · 19,460 reports
More interview insightsJob description
Overview
Skill required: Tech for Operations - Artificial Intelligence (AI)
Qualifications
- 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.
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Job application FAQ
Last updated 4 October 2026
- How do I apply for AI/ML Computational Science Analyst at Accenture in India?
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- Is AI/ML Computational Science Analyst remote or on-site in Gurugram, Haryana?
- This listing is marked on_site with location Gurugram, Haryana. Confirm details on the job description before applying.
- What skills matter for this AI/ML Computational Science Analyst role at Accenture in India?
- Key skills for this role include skills listed in the job description. Upload a resume to see which requirements you already meet and which are gaps.