15 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategy, target-state architecture, investment roadmaps, reference architectures, and implementation sequencing.
- Own end-to-end technical architecture for complex AI platforms spanning data, models, applications, infrastructure, and operating models.
- Set Azure AI and LLM platform strategy using Foundry, Azure OpenAI, RAG, agentic workflows, API integration, identity, networking, and governance.
- Establish architecture for multi-agent systems, orchestration, memory, tool use, agent registries, AI gateways, control planes, and certification gates.
- Define context-layer architecture covering knowledge graphs, ontologies, vector search, semantic retrieval, prompt and context assembly, conversational state, and reusable memory services.
- Establish guardrails and controls for identity, authorization, prompt-injection defense, PII protection, audit logging, lineage, risk management, and responsible AI.
- Mandate 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, budget enforcement, cost planning, alerts, and optimization.
- Lead multiple domain architects and senior SMEs and produce authoritative architecture blueprints, ADRs, diagrams, solution patterns, interface specifications, and governance playbooks.
Requirements
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- 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 AI, data, analytics, or cloud-native solutions using a cloud vendor.
- At least 2 years of LLM and generative AI solution architecture experience, including agentic systems, RAG, prompt engineering, model integration, and evaluation.
- At least 2 years architecting and operationalizing LLM-driven applications 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.
- 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 architecture and hands-on 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.
- Expertise in RAG, embeddings, vector databases, semantic retrieval, context engineering, model routing, agent orchestration, memory, tool calling, AI gateways, and model evaluation.
- Experience defining non-functional requirements and controls for performance, scalability, security, privacy, reliability, governance, observability, resiliency, cost optimization, and operational readiness.
- 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 includes Azure Solutions Architect Expert, Azure AI Engineer Associate, or Azure Developer certification; 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, MLflow, FastAPI, Docker, Kubernetes, responsible AI, model risk management, 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, 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.