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.

Key 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.

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 rapid in a POD-based delivery model.

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.

Skill Area Skill Requirement Addl Notes

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 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

📌 AI/ML Engineer (Bengaluru)
🏢 Diensten Tech
📍 Bengaluru