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 contemporary, 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 solid 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.
- Positive 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.
- Robust 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.
- Solid 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

About Our Company | Accenture

📌 Senior Lead Full Stack AI Engineer 2 (Kochi)
🏢 Accenture
📍 Kochi