Overview

Job Title – Senior Lead Full Stack AI Engineer – Associate Manager - ACS SONG

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.
• 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.

Education

• Job Summary
• 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.