15 hours 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, delivery dependencies, and client expectations.
- Design GCP generative AI reference 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 covering 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, component and sequence 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 for the project role; required qualifications also specify 10+ years in software engineering, data engineering, AI/ML engineering, or technology architecture.
- At least 5 years of experience designing and deploying enterprise-grade advanced AI or cloud data solutions using a cloud vendor, 2+ years in agentic AI/LLM/generative AI architecture or engineering delivery, and 4+ years of Python coding experience.
- At least 4 years of experience in machine learning, deep learning, NLP, data engineering, analytical engineering, or AI product delivery.
- Demonstrated solution or technology architecture experience in industry contexts such as financial services, healthcare, retail, manufacturing, telecom, or media.
- Hands-on experience with Google Cloud AI and data services including 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.
- Strong knowledge of RAG, embeddings, vector databases, prompt engineering, model routing, fine-tuning or adaptation, function calling, tool integration, and agent orchestration.
- Experience designing reusable agent services, memory services, API gateways, integration adapters, orchestration layers, evaluation harnesses, deployment pipelines, CI/CD, infrastructure-as-code, automated testing, model evaluation, MLOps/LLMOps, monitoring, and production release governance.
- Preferred qualifications include Google Cloud certifications, BigQuery semantic layers, Dataform or dbt, Vertex AI Pipelines, LangChain, LlamaIndex, GCP security perimeter controls, LangGraph, Haystack, Semantic Kernel, MLflow, FastAPI, Docker, Kubernetes, responsible AI, model risk management, red-teaming, human-in-the-loop review, A/B testing, and GenAI FinOps.
- Strong stakeholder management and communication skills are required for explaining architecture trade-offs, risks, and recommendations to engineering, product, security, and leadership teams.
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.