You will be taken to Accenture’s own careers page to apply. Job ID R00363197.
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.
Professional and Technical Skills
Minimum 8 years of professional software engineering experience, with strong experience in full-stack application development, cloud-native applications, backend engineering, and enterprise system integration.
Strong hands-on experience developing and deploying solutions on Google Cloud Platform (GCP).
Experience building production-grade applications using modern frontend frameworks, backend technologies, APIs, databases, cloud services, and distributed application architectures.
Hands-on experience integrating AI, Generative AI, LLM, Machine Learning, or intelligent search capabilities into enterprise applications.
Experience working with Vertex AI, Gemini models, or equivalent cloud-based Generative AI platforms.
Experience designing and implementing scalable, secure, highly available, and maintainable cloud-native applications.
Experience working within enterprise software development environments using Agile delivery, Git-based development, automated testing, CI/CD, and DevOps practices.
Experience with AI/ML and cloud services on AWS or Microsoft Azure is an added advantage.
Strong hands-on experience with Google Cloud Platform, particularly services such as Vertex AI, Cloud Run, GKE, Cloud Functions, Pub/Sub, BigQuery, Cloud Storage, Cloud SQL, Firestore, IAM, Secret Manager, Cloud Logging, and Cloud Monitoring.
Hands-on experience with Vertex AI and Gemini models, including model APIs, prompt engineering, model configuration, embeddings, grounding, and integration of Generative AI capabilities into applications.
Strong frontend development experience using React, Angular, Next.js, TypeScript, JavaScript, HTML, CSS, or equivalent modern frontend technologies.
Strong backend programming experience using Python, Java, Node.js/TypeScript, or equivalent enterprise development technologies.
Experience with backend frameworks such as FastAPI, Flask, Django, Spring Boot, Express.js, NestJS, or comparable frameworks.
Strong experience designing and developing REST APIs, microservices, event-driven applications, asynchronous processing, and enterprise integration services.
Good understanding of Retrieval-Augmented Generation (RAG), embeddings, vector search, semantic search, document ingestion, chunking strategies, prompt engineering, and LLM application patterns.
Experience with vector databases or search technologies such as Vertex AI Vector Search, AlloyDB AI, Elasticsearch/OpenSearch, Pinecone, Weaviate, pgvector, or equivalent technologies.
Experience working with relational and NoSQL databases such as BigQuery, Cloud SQL, AlloyDB, PostgreSQL, MySQL, Firestore, MongoDB, or equivalent technologies.
Experience with containerization and orchestration technologies such as Docker and Kubernetes/GKE.
Experience with asynchronous and event-driven architectures using technologies such as Pub/Sub, Kafka, messaging platforms, event queues, and background workers.
Strong understanding of authentication and authorization concepts including OAuth 2.0, OpenID Connect, JWT, Google Cloud IAM, service accounts, API security, and role-based access control.
Experience with Git-based development, branching strategies, code reviews, automated testing, and CI/CD using Cloud Build, GitHub Actions, GitLab CI, Jenkins, or equivalent technologies.
Experience with Infrastructure as Code using Terraform, Google Cloud Deployment Manager, or equivalent technologies is desirable.
Good understanding of application security, API security, encryption, secrets management, network security, vulnerability management, and secure software development practices.
Strong experience designing and developing complete applications across the presentation, application, integration, data, and cloud infrastructure layers.
Ability to translate UX designs and business requirements into responsive, accessible, reusable, and maintainable frontend components.
Strong understanding of modern frontend architecture including component-based development, state management, API integration, authentication flows, browser performance, and responsive design.
Strong experience designing scalable backend architectures, microservices, APIs, business logic, integration services, and asynchronous processing components.
Ability to integrate frontend applications securely with backend APIs, AI services, databases, enterprise applications, and external systems.
Experience implementing caching, session management, API performance optimization, error handling, retries, resilience patterns, and application-level observability.
Strong understanding of software architecture and engineering principles including SOLID principles, design patterns, separation of concerns, API-first design, scalability, maintainability, and reusable component design.
Ability to troubleshoot applications across the complete technology stack, including browser/UI, frontend services, APIs, backend services, databases, AI services, containers, networking, and cloud infrastructure.
Hands-on experience integrating LLMs and Generative AI services into web and enterprise applications, preferably using Vertex AI and Gemini.
Experience developing AI-enabled application features such as conversational interfaces, enterprise knowledge assistants, intelligent search, summarization, recommendations, information extraction, document processing, and content generation.
Good understanding of prompt engineering, structured outputs, function/tool calling, model parameters, context management, grounding, response validation, and AI application error handling.
Experience designing and implementing RAG architectures using enterprise documents, embeddings, vector databases, metadata filtering, retrieval strategies, and LLM-generated responses.
Understanding of AI application considerations including hallucination management, guardrails, responsible AI, data privacy, content safety, latency, token consumption, model evaluation, and cost optimization.
Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Google Agent Development Kit (ADK), or equivalent AI application frameworks is an added advantage.
Strong experience deploying and operating applications using Cloud Run, GKE, serverless technologies, containers, and other GCP compute services.
Experience building automated CI/CD pipelines covering application builds, testing, containerization, security validation, infrastructure deployment, and production releases.
Experience implementing application monitoring, distributed tracing, centralized logging, health checks, alerts, dashboards, and production observability.
Good understanding of cloud architecture patterns covering high availability, scalability, disaster recovery, fault tolerance, network design, API management, caching, and performance optimization.
Exposure to Agentic AI concepts, Model Context Protocol (MCP), Agent2Agent (A2A) communication, tool calling, and AI workflow orchestration is desirable.
Ability to analyze and optimize cloud resource utilization, AI model consumption, API usage, application performance, and overall operating cost.
Strong analytical, troubleshooting, system-design, and problem-solving skills with the ability to investigate issues across the complete application stack.
Ability to work effectively with solution architects, AI engineers, data engineers, UX designers, DevOps engineers, security teams, product owners, and business stakeholders.
Strong communication skills in English with the ability to explain application architecture, AI concepts, technical designs, implementation approaches, risks, and trade-offs clearly.
Ability to provide technical guidance, conduct code and design reviews, mentor junior engineers, and contribute to engineering standards and best practices.
Proactive mindset with ownership of application design, development, technical quality, production issues, and continuous improvement.
Comfortable working within Agile delivery teams and participating in sprint planning, architecture discussions, technical workshops, code reviews, demonstrations, and production support activities.
Additional Information
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