3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategy, target-state architecture, transformation roadmaps, reference architectures, and implementation sequencing.
- Architect GCP-based AI platforms using Gemini, Vertex AI, BigQuery, Vertex AI Search, vector stores, GKE, and Cloud Run.
- Lead architecture for agentic 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.
- Establish identity, authorization, guardrails, prompt-injection defenses, PII protection, audit logging, lineage, and other AI security controls.
- Set standards for evaluation and observability covering accuracy, relevance, groundedness, quality, latency, cost, safety, reliability, and production support.
- Establish AI FinOps practices including usage labeling, token budgets, budget controls, cost planning, alerts, and optimization.
- Produce enterprise AI blueprints, architecture decision records, sequence diagrams, solution patterns, interface specifications, reference architectures, and governance playbooks.
- Advise executive stakeholders and lead domain architects and senior subject-matter experts across AI applications, security, operations, data, and model platforms.
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 advanced AI, data, analytics, or cloud-native solutions using a cloud vendor.
- At least 2 years architecting 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 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.
- Deep hands-on architecture experience with Gemini, Vertex AI, Model Garden, Vertex AI Search, BigQuery, vector search, GKE, Cloud Run, Cloud Functions, Pub/Sub, IAM, Cloud Logging, Cloud Monitoring, and VPC Service Controls.
- Strong expertise in RAG, embeddings, vector databases, semantic retrieval, context engineering, model routing, agent orchestration, memory, tool calling, AI gateways, and model evaluation.
- Preferred experience includes Google Cloud Professional Cloud Architect, Professional ML Engineer, or Professional Data Engineer certification; BigQuery semantic layers; Dataform or dbt; Vertex AI Pipelines; Looker; LangChain or LlamaIndex; regulated-data architecture; and frameworks such as LangGraph, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, and Kubernetes.
- Preferred experience includes responsible AI, model risk management, AI governance, red-teaming, synthetic data, human-in-the-loop review, A/B testing, GenAI FinOps, and recognized enterprise architecture thought leadership.
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.