Senior Lead Full Stack AI Engineer 2

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