Data Science Practitioner

Accenture — Hyderabad

Posted 2026-11-04

Project Role : Data Science Practitioner
Project Role Description : Formulating, design and deliver AI/ML-based decision-making frameworks and models for business outcomes. Measure and justify AI/ML based solution values.
Must have skills : Data Science
Good to have skills : NA
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
AI Engineering Lead / Solution Architect – Knowledge Graph & Agentic AI
Experience: 12+ years overall, including 5+ years hands-on in production GenAI/Agentic AI
Location: India
Role Summary
Own enterprise AI architecture and platform strategy for agentic, multi-agent, and knowledge-graph-backed systems. Lead the full AI lifecycle — use-case discovery, architecture, engineering, governance, security, and production optimization — while mentoring engineering teams and representing architecture decisions to senior stakeholders.
Key Responsibilities
Define enterprise AI/agentic architecture standards, security, governance, and observability frameworks across engagements
Lead design reviews for GenAI, RAG, and multi-agent solutions, from high-level architecture through detailed design
Architect and oversee production-grade agentic / multi-agent AI platforms (e.g., CrewAI, LangGraph, or equivalent orchestration frameworks) supporting high-concurrency, low-latency enterprise workloads
Design and integrate knowledge graph / semantic layer components (e.g., Neo4j or equivalent) for retrieval, reasoning, and data grounding across agentic workflows
Build reusable AI platform capabilities — LLMOps, model evaluation, and observability practices — and drive enterprise adoption roadmaps
Own responsible-AI, security (RBAC, SSO, audit logging, encryption), and compliance posture for regulated-industry deployments
Mentor AI engineers/architects on agentic AI, RAG, and cloud AI engineering practices
Partner with client/executive stakeholders to translate business priorities into an AI delivery roadmap
Champion technical innovation and reusable frameworks across the AI engineering practice
Required Skills & Experience
12+ years in software/AI engineering, with 5+ years hands-on in production GenAI, RAG, and agentic/multi-agent systems
LLM engineering: GPT/Claude/LLaMA-class models, prompt engineering, function/tool calling, RAG architecture
Agentic AI frameworks (e.g., CrewAI, LangGraph, or equivalent) working knowledge of agent-to-agent and tool-calling protocols (e.g., MCP, A2A)
Hands-on depth with Azure AI and integration services — Azure AI Foundry, Azure Functions, Azure Logic Apps — or equivalent AWS/GCP AI and serverless stacks
Knowledge graph / semantic technologies — Neo4j or equivalent, graph-based retrieval and ontology patterns
Strong programming: Python, TypeScript/Node.js, FastAPI, React.js, SQL
Data layer: PostgreSQL, MongoDB, and cloud-native relational/NoSQL stores, plus vector/AI-search indexing
AI evaluation and observability practices for production LLM systems
Track record owning enterprise architecture, security, and governance for regulated industries
Experience mentoring engineers and presenting to executive stakeholders
Preferred
Hands-on experience with Neo4j (or an equivalent graph database) and knowledge graph / ontology design for enterprise semantic layers
Production deployment experience across multiple major cloud platforms (AWS, Azure, and GCP) — spanning AI/ML services, managed databases, and serverless compute
Experience integrating real-time conversational voice or avatar interfaces into enterprise applications
Familiarity with no-code/low-code AI platforms (e.g., Microsoft Power Platform, Copilot Studio, or equivalent) and how they fit into an enterprise agentic AI strategy
Relevant cloud architecture or graph-database certifications
Education
Bachelor's/Master's in Computer Science, Engineering, or related field

15 years full time education

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