3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategy, target-state architecture, reference architectures, investment roadmaps, and implementation sequencing with client executives and business leaders.
- Own end-to-end technical architecture for complex AI platforms spanning data, models, applications, platforms, operating models, and enterprise integration.
- Define GCP architectures using Gemini, Vertex AI, BigQuery, Vertex AI Search, vector stores, GKE, Cloud Run, and related services.
- Set architectural direction for multi-agent systems, orchestration, memory, tool use, agent registries, AI gateways, control planes, risk scoring, and certification gates.
- Design context-layer capabilities including 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, security, and governance controls.
- 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, cost planning, threshold alerts, and optimization controls.
- Lead multiple domain architects and senior subject-matter experts across AI applications, security and trust, operations, data and knowledge engineering, model platforms, and inference.
- Produce and steward enterprise AI blueprints, architecture decision records, sequence diagrams, solution patterns, interface specifications, reference architectures, 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.
- Minimum 18 years of overall experience is required.
- At least 15 years of 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 experience in LLM and generative AI solution architecture, 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.
- At least 6 years as a machine learning, data, or AI architect designing large-scale analytical engineering solutions in relevant industry contexts.
- Deep architecture and hands-on engineering experience with GCP AI services, Gemini, Vertex AI, model platforms, vector search, Kubernetes-based platforms, messaging, identity, logging, monitoring, and network security controls.
- 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, reference architectures, technology selections, agent registries, AI control planes, guardrails, evaluation stacks, model-risk controls, and cross-platform governance.
- Strong executive communication, architecture governance, thought leadership, and stakeholder influence skills.
- Preferred experience or certification involving Google Cloud Professional Cloud Architect, Professional ML Engineer, or Professional Data Engineer; BigQuery semantic layers; Dataform or dbt; Vertex AI pipelines; Looker; AI orchestration frameworks; responsible AI; model risk management; red-teaming; synthetic data; human-in-the-loop review; A/B testing; and GenAI FinOps.
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.