Full Stack Engineer with AI

This role requires a full-stack engineering mindset, bridging AI model integration, cloud development, and web application deployment, ensuring flexibility across GCP and Azure .

Key Responsibilities
• Develop AI-Driven Applications: Build full-stack solutions integrating AI models into web applications, APIs, and cloud services.
• Cloud-Native Development: Design and deploy scalable microservices, APIs, and user-facing applications on GCP (Cloud Run, GKE, App Engine) and Azure     (AKS, App Service, Functions).
• Backend Engineering: Develop efficient and scalable backend services using Python (Flask, FastAPI), Node.js (Express), or Java (Spring Boot) with databases like BigQuery, Firestore, PostgreSQL, or Azure Cosmos DB.
• Frontend Engineering: Design intuitive user interfaces with React, Angular, or Vue.js, ensuring performance and accessibility.
• AI Model Integration: Work with ML engineers to integrate machine learning models into applications using Vertex AI, Azure Machine Learning, or custom APIs.
• MLOps & CI/CD: Implement CI/CD pipelines (Cloud Build, GitHub Actions, Azure DevOps) and automate model deployment.
• Security & Compliance: Ensure AI solutions comply with data privacy, governance, and security best practices on both GCP and Azure.
• AI as a Service (AIaaS): Develop APIs and microservices that expose AI capabilities for use across multiple business units.
• Cross-Cloud Flexibility: Adapt to a hybrid cloud environment, ensuring applications can run across GCP and Azure     without disruption.
• Collaboration & Experimentation: Work in an agile setup, collaborating with data scientists, cloud engineers, and business teams to prototype, test, and deploy AI solutions.

Key Qualifications & Skills
• 3-6 years of experience as a Full Stack Engineer, Cloud Engineer, or AI Software Engineer.
• Cloud Expertise (GCP & Azure    ):
• GCP services: Cloud Run, Kubernetes (GKE), BigQuery, Firestore, IAM, Pub/Sub.
• Azure     services: Azure Kubernetes Service (AKS), App Service, Azure Machine Learning, Azure Functions, Cosmos DB.
• Backend Development: Proficiency in Python (Flask, FastAPI), Node.js (Express), or Java (Spring Boot) with database experience in PostgreSQL, Firestore, Cosmos DB.
• Frontend Development: Hands-on experience with React, Angular, or Vue.js, including state management and UI performance optimization.
• API Development: Strong experience in RESTful API, GraphQL, and gRPC development.
• MLOps & AI Integration: Experience with Vertex AI, TensorFlow Serving, MLFlow, or Azure ML Model Deployment.
• DevOps & CI/CD: Experience with Cloud Build, GitHub Actions, Azure DevOps, Terraform for infrastructure automation.
• Security & Data Privacy: Knowledge of OAuth, IAM, JWT, data encryption, and regulatory compliance (e.g., GDPR, MAS TRM).
• Hybrid & Multi-Cloud Experience: Ability to design and deploy applications that run across GCP and Azure.

Preferred Qualifications
• Experience working in financial services, insurance, or regulated environments.
• Exposure to containerization (Docker, Kubernetes, Helm) and serverless architectures.
• Experience in LLMs, Generative AI, and AI observability is a plus.
• Knowledge of AI governance, explainability, and responsible AI practices.