3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategy, target-state architecture, transformation roadmaps, and investment sequencing with executives and business leaders.
- Lead enterprise AI assessments, technology comparisons, platform selection, reference architecture development, and modernization planning.
- Define GCP architectures using Gemini, Vertex AI, BigQuery, Vertex AI Search, vector stores, GKE, and Cloud Run for enterprise agents, RAG, and inference.
- Own cohesive end-to-end technical solutions for complex AI platforms and translate architecture principles into buildable technical designs.
- Set architectural direction for multi-agent systems, orchestration, memory, tool use, agent registries, AI gateways, 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.
- Mandate evaluation and observability practices for accuracy, relevance, groundedness, model quality, latency, cost, safety, reliability, and production support.
- Establish AI FinOps practices including usage labeling, token budgets, gateway-enforced budgets, alerts, and optimization mechanisms.
- Produce 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 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 industry contexts.
- Deep hands-on architecture experience with Gemini Enterprise Agent Platform, Vertex AI, Gemini models, Model Garden, Vertex AI Search, BigQuery, AlloyDB, 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 enterprise non-functional requirements and 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 experience with Google Cloud Professional Cloud Architect, Professional ML Engineer, or Professional Data Engineer certifications; BigQuery semantic layers, Dataform, dbt, Vertex AI Pipelines, Looker, LangChain, LlamaIndex, VPC Service Controls, and regulated data architecture.
- Preferred exposure to LangChain, LangGraph, LlamaIndex, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, and Kubernetes.
- Preferred experience with responsible AI, model risk management, AI governance boards, 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.