3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define enterprise AI strategies, target-state architectures, transformation roadmaps, and investment plans with executive and business stakeholders.
- Lead AI assessments, platform and technology selection, reference architecture development, modernization planning, and implementation sequencing.
- Define GCP enterprise AI architectures 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, agent registries, AI gateways, control planes, risk scoring, and certification gates.
- Define context-layer architectures 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 including usage labeling, token budgets, cost thresholds, alerts, and optimization levers.
- Produce and steward 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 of experience 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 of experience 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 of experience as a machine learning, data, or AI architect designing large-scale analytical engineering solutions.
- Deep architecture and hands-on experience with Google Cloud AI, data, infrastructure, identity, logging, monitoring, and networking services.
- 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 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, production evaluation stacks, model risk controls, and cross-platform governance.
- Strong executive communication, architecture governance, thought leadership, and stakeholder influence skills.
- Preferred qualifications include Google Cloud professional certifications, experience with Dataform or dbt, Vertex AI Pipelines, Looker, LangChain or LlamaIndex, regulated-data architecture, open-source AI 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.