Senior Lead Full Stack AI Engineer 2

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       

 

About Our Company | Accenture

 

 

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