3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Translate business strategy and product goals into technical visions, architecture blueprints, non-functional requirements, and implementation roadmaps.
- Lead stakeholder workshops covering feasibility, scope, solution boundaries, dependencies, and client expectations.
- Design GCP generative AI architectures using Gemini, Model Garden, BigQuery, Vertex AI Search, vector stores, Cloud Run, and GKE.
- Architect model- and tool-agnostic multi-agent systems with orchestration, tool use, memory, context management, MCP/control-plane patterns, and reusable service abstractions.
- Design data and context layers for ingestion, preprocessing, synchronization, chunking, embeddings, vector search, knowledge graphs, and semantic retrieval.
- Define evaluation frameworks for accuracy, relevance, faithfulness, groundedness, latency, cost, safety, security, and operational reliability.
- Establish AI security, governance, and observability controls including guardrails, prompt-injection defense, PII protection, access control, audit logging, and tracing.
- Maintain architecture decision records, diagrams, design specifications, integration patterns, and reusable reference architecture assets.
Requirements
- Bachelor's degree or equivalent in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology, or a related engineering discipline.
- At least 12 years of experience is stated in the project requirements; the detailed qualifications require at least 10 years in software engineering, data engineering, AI/ML engineering, or technology architecture.
- At least 5 years designing and deploying enterprise-grade advanced AI or cloud data solutions using a cloud vendor.
- At least 2 years in agentic AI, LLM, and generative AI solution architecture or engineering delivery.
- At least 4 years of Python coding experience and experience with APIs, distributed systems, reusable frameworks, and cloud-native application patterns.
- At least 4 years of experience in ML, deep learning, NLP, data engineering, analytical engineering, or AI product delivery.
- Hands-on experience with Google Cloud AI, data, compute, networking, identity, logging, monitoring, and governance services.
- Strong knowledge of RAG, embeddings, vector databases, prompt engineering, model routing, fine-tuning or adaptation, function calling, tool integration, and agent orchestration.
- Experience with reusable agent services, memory services, API gateways, integration adapters, orchestration layers, evaluation harnesses, deployment pipelines, CI/CD, infrastructure-as-code, automated testing, MLOps/LLMOps, monitoring, and production release governance.
- Experience as a solution or technology architect in an industry context such as financial services, healthcare, retail, manufacturing, telecom, or media.
- Preferred experience includes Google Cloud certifications, BigQuery semantic layers, Dataform or dbt, Vertex AI Pipelines, LangChain, LlamaIndex, GKE, GCP security perimeter controls, open-source AI frameworks, responsible AI, model risk management, red-teaming, 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.