2 days ago
Bengaluru, IndiaSenior
Responsibilities
- Configure and develop Gemini Enterprise applications, data stores, connectors, ingestion services, and access-control behavior.
- Build Python services, APIs, Gemini and Vertex AI integrations, agents, prompts, structured outputs, tool calls, workflows, and human-in-the-loop controls.
- Develop and tune RAG and enterprise-search pipelines covering parsing, chunking, embeddings, retrieval, reranking, grounding, and source attribution.
- Create secure integrations with enterprise APIs, databases, SaaS products, and line-of-business applications using authentication, permissions, validation, retries, and duplicate-action prevention.
- Implement IAM, service identities, OAuth, secrets management, least-privilege access, environment separation, Responsible AI, privacy, auditability, and governance controls.
- Create evaluation, quality, security, regression-testing, release, monitoring, logging, tracing, and cost-optimization practices for deployed AI systems.
- Conduct design and code reviews, mentor engineers, document operational patterns, and develop reusable templates and reference implementations.
Requirements
- At least 7.5 years of experience with Large Language Models, with 6 to 10 years of overall experience including 3 years in Python backend cloud integration or platform engineering.
- Hands-on Google Cloud delivery experience with Gemini or Vertex AI APIs and suitable application hosting such as Cloud Run or an agent runtime.
- Strong Python and API development skills with working knowledge of SQL, JSON, Git, automated testing, error handling, and maintainable service design.
- Practical experience implementing RAG or enterprise search, including document processing, embeddings, retrieval quality, grounding, structured outputs, and prompt engineering.
- Experience with tool-using LLM applications, agent orchestration, workflow state, function calling, enterprise-system integrations, and production deployments.
- Working knowledge of IAM, SSO, OAuth, service identities, secrets, source-level permissions, containers, infrastructure as code, logging, tracing, monitoring, and LLMOps practices.
- Experience mentoring engineers, reviewing technical deliverables, troubleshooting systems, and collaborating with architects, product, security, and business teams.
- Preferred qualifications include Gemini Enterprise or Google Agentspace implementation, Google ADK, Vertex AI Search or Discovery Engine APIs, MCP or A2A integrations, LangGraph, LangChain, multi-agent orchestration, enterprise knowledge management, taxonomy, ontology, knowledge graphs, GenAI evaluation, observability, safety, governance, Responsible AI frameworks, or relevant certifications.
Benefits
- The position is based at the Bengaluru office.
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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.