AI/ML Engineer

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 fast 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