3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategy, target-state architecture, reference architectures, and investment roadmaps with client executives and business leaders.
- Lead enterprise AI assessments, technology comparisons, platform selection, modernization planning, and implementation sequencing.
- Define GCP AI platform architecture using Gemini, Vertex AI, BigQuery, Vertex AI Search, vector stores, GKE, and Cloud Run.
- Architect model- and tool-agnostic multi-agent systems, including orchestration, memory, tool use, registries, control planes, risk scoring, 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 identity, authorization, guardrails, prompt-injection defenses, PII protection, audit logging, lineage, and defense-in-depth controls.
- Set evaluation and observability standards for accuracy, relevance, groundedness, model quality, latency, cost, safety, reliability, and production support.
- Establish AI FinOps practices covering usage labeling, token budgets, cost planning, threshold alerts, and optimization.
- Produce enterprise AI blueprints, architecture decision records, sequence diagrams, solution patterns, interface specifications, reference architectures, and governance playbooks.
- Lead multiple domain architects and senior SMEs across agentic applications, 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.
- 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 at least one cloud vendor.
- At least 2 years of LLM and generative AI solution architecture experience, including agentic systems, RAG, prompt engineering, model integration, and evaluation patterns.
- 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 applications.
- 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 Vertex AI, Gemini, Model Garden, Vertex AI Search, BigQuery, AlloyDB or Cloud SQL vector search, GKE, Cloud Run, Cloud Functions, Pub/Sub, IAM, Cloud Logging, Cloud Monitoring, and VPC Service Controls.
- 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 architectural controls for performance, scalability, security, privacy, reliability, governance, observability, resiliency, cost optimization, and operational readiness.
- Experience establishing agent registries, certification models, AI control planes, access models, guardrails, evaluation stacks, model risk controls, and cross-platform governance.
- Strong executive communication, architecture governance, and thought leadership skills.
- 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; regulated data architecture; LangGraph, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, or Kubernetes; responsible AI and 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.