3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategy, target-state architecture, investment roadmaps, and implementation sequencing with executive and business stakeholders.
- Lead enterprise AI assessments, technology comparisons, platform selection, modernization planning, and reference architecture development.
- Define Azure AI and LLM architectures using Azure AI Foundry, Azure OpenAI, agentic workflows, RAG, tool integration, context assembly, and enterprise knowledge grounding.
- Own end-to-end technical solutions for complex AI platforms across models, data, applications, platforms, operating models, and enterprise integration.
- Set architectural direction for multi-agent systems, orchestration, memory, tool use, agent registries, AI gateways, control planes, risk scoring, and certification gates.
- Define context-layer architectures covering knowledge graphs, ontologies, vector search, semantic retrieval, prompt and context assembly, conversational state, and reusable memory services.
- Establish identity, authorization, guardrails, prompt-injection defenses, PII protection, audit logging, lineage, and defense-in-depth controls.
- Mandate AI evaluation and observability practices covering accuracy, relevance, groundedness, model quality, latency, cost, safety, reliability, and production support.
- Establish AI FinOps practices including usage labeling, token budgets, gateway-enforced budgets, threshold alerts, and cost optimization.
- Produce and steward enterprise AI blueprints, architecture decision records, sequence diagrams, solution patterns, interface specifications, reference architectures, and governance playbooks.
- Lead multiple domain architects and senior subject-matter experts across agentic application design, AI security and trust, AI operations, data and knowledge engineering, model platforms, and inference.
Requirements
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- Minimum 18 years of experience is required.
- At least 15 years of overall experience across software engineering, data engineering, AI/ML engineering, cloud architecture, enterprise architecture, or technology leadership.
- At least 8 years designing and deploying enterprise-grade advanced AI, data, analytics, or cloud-native solutions using a cloud vendor.
- At least 2 years designing LLM and generative AI solutions, including agentic systems, RAG, prompt engineering, model integration, and evaluation patterns.
- At least 2 years architecting and operationalizing LLM-driven application architectures in enterprise-scale or production environments.
- At least 6 years of experience in engineering, machine learning, deep learning, NLP, data engineering, or large-scale analytical engineering applications.
- At least 6 years as a machine learning, data, or AI architect designing large-scale analytical engineering solutions in industry contexts such as banking, insurance, healthcare, public sector, retail, or energy.
- Deep hands-on architecture and engineering experience with Microsoft Foundry, Azure AI Foundry, Foundry Agent Service, Azure OpenAI models, Azure AI Search, Prompt Flow, Azure Functions, Logic Apps, API Management, Event Grid, Microsoft Fabric, Entra ID, Key Vault, Azure Monitor, and Application Insights.
- Strong expertise in RAG, embeddings, vector databases, semantic retrieval, context engineering, model routing, agent orchestration, memory, tool calling, AI gateways, and model evaluation.
- Experience establishing enterprise non-functional requirements and controls for performance, scalability, security, privacy, reliability, governance, observability, resiliency, cost optimization, and operational readiness.
- Experience making evidence-based decisions on design patterns, reference architectures, frameworks, technology selections, foundation models, and deployment approaches.
- Experience with agent registries, certification models, AI control planes, access models, guardrails, production evaluation stacks, model risk controls, and cross-platform governance.
- Strong executive communication, architecture governance, and thought leadership skills.
- Preferred experience with Azure Solutions Architect Expert, Azure AI Engineer Associate, or Azure Developer certifications; Microsoft Fabric, Semantic Kernel, Prompt Flow, private endpoints, Defender for Cloud, Microsoft 365 Copilot extensibility, Responsible AI governance, and enterprise integration patterns.
- Preferred exposure to LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, and Kubernetes.
- Preferred experience with responsible AI, model risk management, AI governance boards, red-teaming, synthetic data, human-in-the-loop review, A/B testing, and GenAI FinOps.
- Recognized thought leadership through enterprise reference architectures, capability building, client advisory, platform accelerators, publications, whitepapers, conference sessions, or industry forums.
Tech Stack
Categories
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.