Senior Lead Full Stack AI Engineer

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, J