AI/ML Computational Science Sr Analyst

About the Role

The AI/ML Computational Science Sr Analyst Agentic AI & Integration will design, build, integrate, test, and deploy AI-native and Agentic AI solutions for Accenture Operations RDE PODs. This role is 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 operate close to client problems, contribute across the delivery lifecycle, reduce handoffs, and accelerate client-facing outcomes through compact, T-shaped teams.

Key Responsibilities

• Independently develop Python-based AI/ML modules, reusable scripts, APIs, and integration components.
• Build and test GenAI components including prompts, structured outputs, RAG pipelines, and vector search integrations.
• Support agentic workflows involving tool calling, orchestration, state handling, and function routing.
• Participate in CI/CD, Docker-based packaging, deployment support, and cloud-native engineering activities.
• Own assigned build-test-deploy tasks and collaborate with L9/L8 engineers on production readiness.

Requirements & Tech Stack

• Must Have Skills:

• Python & Full-Stack Development

• Agentic AI (LangChain, LangGraph, MCP, RAG)

• Strong Python programming experience including async programming, OOP, data structures, and scripting.

• Hands-on exposure to GenAI / LLM solutioning, prompt engineering, structured outputs, and RAG implementation.

• Experience developing REST APIs, microservices, or integration components.

• Working knowledge of Git, CI/CD, Docker, and cloud deployment concepts.

• Ability to independently own modules and collaborate effectively in POD-based delivery.

• What are we looking for:

• Agentic AI Concepts:

• Deep understanding of AI agent design, reasoning loops, orchestration patterns & multi-agent coordination architectures.

• Tool calling, function routing, agent memory & state management, autonomous decision-making patterns.

• LLM & Prompt Engineering:

• Hands-on with LLMs (e.g., GPT-4, Claude, Gemini); prompt engineering, few-shot, chain-of-thought & structured output techniques.

• RAG pipeline design, vector database integration (Pinecone, Weaviate, ChromaDB) & semantic search for enterprise grounding.

• 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, but Python strongly preferred for AI workloads.

• REST/GraphQL API development, microservices design & enterprise application integration patterns.

• Cloud & DevOps:

• Azure / AWS / GCP hands-on experience; cloud-native architecture, infrastructure provisioning & managed AI services. AWS preferred, cloud-agnostic skills valued.

• Containerization (Docker, Kubernetes), CI/CD pipeline setup, GitOps & automated deployment practices.

• 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).

• Testing & Observability:

• AI solution testing, LLM output evaluation, observability (tracing, monitoring), performance tuning & cost optimization.

• Secondary Skills:

• Exposure to LangGraph, LangChain, Semantic Kernel, CrewAI, or similar AI frameworks.

• Understanding of vector databases, semantic search, AI testing and monitoring concepts.

• Exposure to Azure, AWS, or GCP, Kubernetes, and enterprise integration platforms is preferred.

Qualifications

• BE / BTech / BCA
• English (International) - Intermediate