16 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, operating models, and enterprise integration.
- Define GCP AI platform architecture using Gemini, Vertex AI, BigQuery, Vertex AI Search, vector stores, GKE, and Cloud Run.
- Establish architectures 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 defense, PII protection, audit logging, lineage, security, evaluation, observability, reliability, and governance controls.
- Lead enterprise AI assessments, technology comparisons, platform selection, modernization planning, and executive advisory engagements.
- Produce and maintain 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.
- 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 application patterns 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 settings.
- Deep architecture and hands-on engineering experience with Google Cloud AI services, enterprise AI platforms, RAG, embeddings, vector databases, semantic retrieval, agent orchestration, memory, tool calling, AI gateways, and model evaluation.
- Experience defining non-functional requirements and architectural controls for performance, scalability, security, privacy, reliability, governance, observability, resiliency, cost optimization, and operational readiness.
- Preferred qualifications include Google Cloud Professional Cloud Architect, Professional ML Engineer, or Professional Data Engineer certification experience; BigQuery semantic layers; Dataform or dbt; Vertex AI pipelines; Looker; LangChain or LlamaIndex; and regulated-data architecture.
- Preferred experience includes LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, Kubernetes, responsible AI, model risk management, red-teaming, synthetic data, human-in-the-loop review, A/B testing, and GenAI FinOps.
- Strong executive communication, architecture governance, thought leadership, client advisory, and cross-functional influence skills are preferred.
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.