Senior Lead Full Stack AI Engineer

Accenture in India; Ernakulam; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Accenture in India seeks a Senior Lead AI Full Stack Engineer with 8+ years of software experience, focusing on Google Cloud Platform (GCP) and AI-enabled applications. You will lead design and development across frontend and backend, integrating Vertex AI, Gemini models, and RAG pipelines.

Collaboration with architects, data engineers, and UX teams is key. The role emphasizes scalable, secure, production-ready AI applications with modern frameworks (React/Next.js, Python/Java) and cloud-native

Qualifications

• 8–12 years of software engineering experience.
• Educational qualifications: B.Tech/BE/M.Tech/MCA.
• Hands-on experience with frontend and backend development in cloud-native AI apps.

Responsibilities

• Design and develop full-stack AI applications on GCP.
• Integrate Vertex AI, Gemini models, and Generative AI services.
• Build scalable frontend and backend components using modern frameworks.
• Ensure secure, observable, and automated deployments across environments.
• Develop data persistence with BigQuery/Cloud SQL/Firestore as appropriate.
• Implement retrieval-augmented generation and AI-powered workflows.
• Collaborate with architects, AI engineers, and DevOps teams.

Skills

Full Stack Dev

GCP

Generative AI

Frontend development

Backend services

APIs

Microservices

React

Next.js

Python

Java

Node.js

FastAPI

Spring Boot

Education

B.Tech/BE/M.Tech/MCA

Tools

Vertex AI

GKE

React

Next.js

Python

Java

Spring Boot

Job description

Job Title – Senior Lead Full Stack AI Engineer – Associate Manager - ACS SONG

Management Level: Level 8 – Associate Manager

Location: Kochi, Coimbatore, Trivandrum

Must have skills: Full Stack Application Development, GCP, Generative AI

Good to have skills: Model Context Protocol (MCP)/ Agent2Agent (A2A)

Experience: 8 - 12 years of experience is required

Educational Qualification: B.Tech/BE/M.Tech/MCA

Job Summary

We are seeking a Senior Lead AI Full Stack Engineer specializing in Google Cloud Platform (GCP) with 8+ years of professional software engineering experience and strong expertise in building modern, cloud-native, AI-enabled applications. The ideal candidate should have hands-on experience across frontend development, backend services, APIs, microservices, databases, cloud infrastructure, and AI/Generative AI integration (at least 2 years of the total years of experience), with GCP as the primary technology platform. The role will focus on designing and developing end-to-end applications that integrate AI capabilities such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent search, conversational interfaces, and AI-powered business workflows. The candidate will work closely with solution architects, AI engineers, data engineers, UX teams, product owners, and DevOps teams to deliver scalable, secure, high-performing, and production-ready AI applications.

Roles And Responsibilities

• Design, develop, and support production-grade full-stack AI applications on GCP, covering frontend interfaces, backend services, APIs, data integration, AI services, and cloud deployment.
• Integrate Vertex AI, Gemini models, Generative AI services, RAG solutions, intelligent search, and AI/ML capabilities into enterprise web and digital applications.
• Build scalable frontend and backend components using modern frameworks and cloud-native architecture patterns while ensuring performance, security, maintainability, and usability.
• Leverage GCP services and DevOps practices to deliver highly available, observable, secure, and automated AI applications across development, testing, and production environments.
• Design, develop, and maintain end-to-end AI-enabled web applications and enterprise solutions on Google Cloud Platform.
• Develop responsive and user-friendly frontend applications using modern frameworks such as React, Angular, Next.js, or equivalent technologies.
• Develop scalable backend services, REST APIs, microservices, and integration layers using technologies such as Python, Java, Node.js, FastAPI, Spring Boot, or equivalent frameworks.
• Integrate applications with Vertex AI, Gemini models, Generative AI APIs, embeddings, RAG pipelines, semantic search, and other AI/ML services available within GCP.
• Design and implement conversational AI, enterprise search, recommendation, summarization, document-processing, and other AI-powered application capabilities.
• Build integrations between AI applications and enterprise systems, databases, APIs, document repositories, third-party services, and internal applications.
• Design and implement data persistence using technologies such as BigQuery, Cloud SQL, AlloyDB, Firestore, Cloud Storage, Memorystore, or other suitable databases and storage services.
• Develop cloud-native solutions using GCP services such as Cloud Run, Google Kubernetes Engine (GKE), Cloud Functions, Pub/Sub, API Gateway, Apigee, Cloud Storage, and Secret Manager.
• Implement Retrieval-Augmented Generation solutions using document ingestion, chunking, embeddings, vector search, metadata filtering, prompt engineering, and knowledge retrieval patterns.
• Implement secure authentication, authorization, API security, identity management, secrets management, and access control using Google Cloud IAM, Identity Platform, OAuth/OIDC, service accounts, and related security services.
• Optimize application and AI solution performance, including frontend responsiveness, backend latency, API performance, model response time, token usage, caching, scalability, and cloud cost.
• Implement automated testing across frontend, backend, APIs, integrations, and AI components using appropriate unit, integration, functional, and end-to-end testing frameworks.
• Debug and resolve issues across UI components, APIs, backend services, AI integrations, databases, cloud infrastructure, authentication flows, network connectivity, and production deployments.
• Implement logging, monitoring, tracing, alerting, and observability using Google Cloud Logging, Cloud Monitoring, Cloud Trace, OpenTelemetry, or equivalent technologies.
• Collaborate with architects, AI engineers, data engineers, product owners, UX designers, security teams, and DevOps engineers while following software engineering, architecture, security, and coding best practices.