Full Stack Engineer with AI

Job highlights

• Preferred Qualifications
• 6 years of experience as a Full Stack Engineer,Cloud Engineer,or AI Software Engineer.
• Hybrid & Multi-Cloud Experience: Ability to design and deploy applications that run across GCP and Azure
• Experience working in financial services,insurance,or regulated environments.
• Experience in LLMs,Generative AI,and AI observability is a plus.

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.

Job Category: Information Technology

Job Type: Full Time

Job Location: Remote

Languages: English

Experience: 5 to 8