LLM Full Stack Engineer
Accenture
- Location
- Onsite (Bengaluru, Karnataka)
- Employment
- Full-time
- Level
- Senior Level
Posted 3 days ago
About the role
Accenture is seeking a Senior LLM Full Stack Engineer to build and deploy secure, scalable enterprise AI solutions using Gemini Enterprise and Google Cloud. The role focuses on developing RAG pipelines, agent workflows, and production-ready applications that integrate with enterprise systems.
Skills
Large Language Models
Python
Google Cloud
Vertex AI
Gemini Enterprise
RAG
API Development
DevOps
SQL
Agent Orchestration
Prompt Engineering
IAM
CI/CD
Infrastructure as Code
Cloud Run
Software Engineering
Full job details
Project Role : LLM Full Stack Engineer
Project Role Description : Develop and integrate applications with Large Language Models (LLMs) and other enterprise services. Leverage front-end and server-side technologies to develop APIs, implement DevOps practices, and optimize Artificial Intelligence (AI) applications.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 6 to 10 years of overall experience including 3 years in Python backend cloud integration or platform engineering
Summary:
We are seeking a Senior Gemini Enterprise Engineer to build, integrate, deploy, and operate enterprise AI solutions using Gemini Enterprise, Google Cloud, Vertex AI, enterprise search, and agent platforms. The role will deliver secure, scalable, and production-ready enterprise search, knowledge assistants, copilots, and AI agents that respect user permissions and meet agreed quality, reliability, performance, and cost targets.
Roles & Responsibilities:
AI Solution Development
Configure and develop Gemini Enterprise applications, data stores, connectors, and custom ingestion services validate metadata, refresh, deletion, and access-control behavior.
Build Python services, Gemini and Vertex AI integrations, and agents using Google Agent Development Kit (ADK) or an appropriate framework, including prompts, structured outputs, tool calls, workflow state, approvals, and human-in-the-loop controls.
Translate solution designs and business requirements into maintainable, scalable, and testable technical implementations.
Enterprise Search, RAG & Agents
Build and tune RAG pipelines covering document parsing, chunking, embeddings, semantic and hybrid retrieval, reranking, grounding, and source attribution.
Diagnose and resolve missing, stale, unauthorized, or poorly ranked evidence and improve retrieval and answer quality.
Develop and deploy custom and multi-agent workflows, tool integrations, and enterprise automation using supported Gemini Enterprise integration patterns.
Integration, Security & Governance
Integrate enterprise APIs, databases, SaaS products, and line-of-business applications using secure and resilient patterns, including authentication, scoped permissions, validation, retries, timeouts, and duplicate-action prevention.
Implement IAM, service identities, OAuth, secrets management, source-level permissions, least-privilege access, and secure environment separation.
Apply Responsible AI, security, privacy, auditability, and governance controls, including protection against prompt injection, data leakage, unauthorized retrieval, and unsafe actions.
Evaluation, DevOps & Production Support
Create automated and human evaluation approaches for grounding, relevance, safety, hallucination management, agent effectiveness, and task completion.
Implement automated quality, security, and regression tests maintain CI/CD, infrastructure as code, configuration and prompt versioning, and release controls.
Instrument logging, tracing, monitoring, latency, consumption, and cost troubleshoot failures, optimize production performance, and support releases and incidents.
Engineering Leadership
Conduct design and code reviews, mentor engineers, and promote maintainable engineering standards across Python services, APIs, retrieval pipelines, agents, and cloud-native components.
Create reusable patterns, accelerators, templates, operational documentation, and reference implementations while ensuring alignment with approved architecture.
Professional & Technical Skills:
Google Cloud and Enterprise AI
Hands-on Google Cloud delivery experience, including Gemini or Vertex AI APIs and suitable application hosting such as Cloud Run or an agent runtime.
Practical experience configuring or developing enterprise AI, search, knowledge-assistant, copilot, or agent solutions.
Software, Search and Agent Engineering
Strong Python and API development skills, with working knowledge of SQL, JSON, Git, automated testing, error handling, and maintainable service design.
Practical RAG or enterprise-search implementation experience and understanding of document processing, embeddings, retrieval quality, grounding, structured outputs, and prompt engineering.
Experience with tool-using LLM applications, agent orchestration, workflow state, function calling, and enterprise-system integrations.
Security, DevOps and Operations
Working knowledge of IAM, SSO, OAuth, service identities, secrets, source-level permissions, and testing of unauthorized access scenarios.
Experience with containers, CI/CD, infrastructure as code, logging, tracing, monitoring, and supporting a deployed service beyond a demonstration.
Understanding of LLMOps practices, including evaluation pipelines, prompt and configuration management, regression testing, release governance, and production monitoring.
Collaboration and Technical Leadership
Strong problem-solving, troubleshooting, communication, and collaboration skills, with the ability to work effectively with architects, product teams, security teams, and business stakeholders.
Experience mentoring engineers, reviewing technical deliverables, and working in agile and geographically distributed delivery teams.
Preferred Experience
Direct Gemini Enterprise or Google Agentspace configuration and implementation experience.
Google ADK, production agent runtimes, Vertex AI Search or Discovery Engine APIs, and MCP or A2A integrations.
LangGraph, LangChain, multi-agent orchestration, enterprise knowledge management, taxonomy, ontology, or knowledge-graph solutions.
GenAI evaluation, observability, safety, governance, or Responsible AI frameworks.
Relevant Google Cloud, GenAI, or AI engineering certification.
Additional Information:
- The candidate should have minimum 7.5 years of experience in Large Language Models (LLMs).
- This position is based at our Bengaluru office.
- 6 to 10 years of overall experience including 3 years in Python backend cloud integration or platform engineering is required.6 to 10 years of overall experience including 3 years in Python backend cloud integration or platform engineering
Project Role Description : Develop and integrate applications with Large Language Models (LLMs) and other enterprise services. Leverage front-end and server-side technologies to develop APIs, implement DevOps practices, and optimize Artificial Intelligence (AI) applications.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 6 to 10 years of overall experience including 3 years in Python backend cloud integration or platform engineering
Summary:
We are seeking a Senior Gemini Enterprise Engineer to build, integrate, deploy, and operate enterprise AI solutions using Gemini Enterprise, Google Cloud, Vertex AI, enterprise search, and agent platforms. The role will deliver secure, scalable, and production-ready enterprise search, knowledge assistants, copilots, and AI agents that respect user permissions and meet agreed quality, reliability, performance, and cost targets.
Roles & Responsibilities:
AI Solution Development
Configure and develop Gemini Enterprise applications, data stores, connectors, and custom ingestion services validate metadata, refresh, deletion, and access-control behavior.
Build Python services, Gemini and Vertex AI integrations, and agents using Google Agent Development Kit (ADK) or an appropriate framework, including prompts, structured outputs, tool calls, workflow state, approvals, and human-in-the-loop controls.
Translate solution designs and business requirements into maintainable, scalable, and testable technical implementations.
Enterprise Search, RAG & Agents
Build and tune RAG pipelines covering document parsing, chunking, embeddings, semantic and hybrid retrieval, reranking, grounding, and source attribution.
Diagnose and resolve missing, stale, unauthorized, or poorly ranked evidence and improve retrieval and answer quality.
Develop and deploy custom and multi-agent workflows, tool integrations, and enterprise automation using supported Gemini Enterprise integration patterns.
Integration, Security & Governance
Integrate enterprise APIs, databases, SaaS products, and line-of-business applications using secure and resilient patterns, including authentication, scoped permissions, validation, retries, timeouts, and duplicate-action prevention.
Implement IAM, service identities, OAuth, secrets management, source-level permissions, least-privilege access, and secure environment separation.
Apply Responsible AI, security, privacy, auditability, and governance controls, including protection against prompt injection, data leakage, unauthorized retrieval, and unsafe actions.
Evaluation, DevOps & Production Support
Create automated and human evaluation approaches for grounding, relevance, safety, hallucination management, agent effectiveness, and task completion.
Implement automated quality, security, and regression tests maintain CI/CD, infrastructure as code, configuration and prompt versioning, and release controls.
Instrument logging, tracing, monitoring, latency, consumption, and cost troubleshoot failures, optimize production performance, and support releases and incidents.
Engineering Leadership
Conduct design and code reviews, mentor engineers, and promote maintainable engineering standards across Python services, APIs, retrieval pipelines, agents, and cloud-native components.
Create reusable patterns, accelerators, templates, operational documentation, and reference implementations while ensuring alignment with approved architecture.
Professional & Technical Skills:
Google Cloud and Enterprise AI
Hands-on Google Cloud delivery experience, including Gemini or Vertex AI APIs and suitable application hosting such as Cloud Run or an agent runtime.
Practical experience configuring or developing enterprise AI, search, knowledge-assistant, copilot, or agent solutions.
Software, Search and Agent Engineering
Strong Python and API development skills, with working knowledge of SQL, JSON, Git, automated testing, error handling, and maintainable service design.
Practical RAG or enterprise-search implementation experience and understanding of document processing, embeddings, retrieval quality, grounding, structured outputs, and prompt engineering.
Experience with tool-using LLM applications, agent orchestration, workflow state, function calling, and enterprise-system integrations.
Security, DevOps and Operations
Working knowledge of IAM, SSO, OAuth, service identities, secrets, source-level permissions, and testing of unauthorized access scenarios.
Experience with containers, CI/CD, infrastructure as code, logging, tracing, monitoring, and supporting a deployed service beyond a demonstration.
Understanding of LLMOps practices, including evaluation pipelines, prompt and configuration management, regression testing, release governance, and production monitoring.
Collaboration and Technical Leadership
Strong problem-solving, troubleshooting, communication, and collaboration skills, with the ability to work effectively with architects, product teams, security teams, and business stakeholders.
Experience mentoring engineers, reviewing technical deliverables, and working in agile and geographically distributed delivery teams.
Preferred Experience
Direct Gemini Enterprise or Google Agentspace configuration and implementation experience.
Google ADK, production agent runtimes, Vertex AI Search or Discovery Engine APIs, and MCP or A2A integrations.
LangGraph, LangChain, multi-agent orchestration, enterprise knowledge management, taxonomy, ontology, or knowledge-graph solutions.
GenAI evaluation, observability, safety, governance, or Responsible AI frameworks.
Relevant Google Cloud, GenAI, or AI engineering certification.
Additional Information:
- The candidate should have minimum 7.5 years of experience in Large Language Models (LLMs).
- This position is based at our Bengaluru office.
- 6 to 10 years of overall experience including 3 years in Python backend cloud integration or platform engineering is required.6 to 10 years of overall experience including 3 years in Python backend cloud integration or platform engineering
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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