Junior AI Platform Engineer

About the Role

The Junior AI Platform Engineer supports the development and operation of platforms used to deploy, integrate, scale, secure, and monitor AI and machine learning solutions. This role combines software engineering, backend development, cloud computing, containerization, DevOps, and AI platform fundamentals.

Key Responsibilities

• Assist in developing backend services supporting AI and machine learning workloads.
• Develop and test REST APIs using Python or another approved programming language.
• Integrate machine learning models and Generative AI services with enterprise applications.
• Package applications and services using Docker and support deployment to Kubernetes environments.
• Assist with asynchronous jobs, background processing, scheduled workloads, and service-to-service communication.
• Support cloud configuration, infrastructure automation, release pipelines, and environment management.
• Implement input validation, error handling, logging, telemetry, and API documentation.
• Support identity, access control, secret management, network security, and responsible data handling.
• Write unit, integration, API, and basic load tests.
• Investigate defects and production issues, escalating complex problems with evidence and impact details.
• Participate in code reviews, sprint planning, backlog refinement, demonstrations, and technical documentation.
• Collaborate with Machine Learning, Product Engineering, Product Support, and business teams.

Essential Job Functions

API and Backend Development

• Build basic REST APIs and backend components.
• Integrate applications with databases, model endpoints, and external services.
• Apply validation, error handling, logging, and API documentation standards.

Cloud and Container Platforms

• Create and maintain Docker configurations.
• Support Kubernetes deployments, services, configuration, secrets, and scaling.
• Learn Azure compute, networking, storage, identity, monitoring, serverless, and AI services.

DevOps and Automation

• Use Git and GitHub-based workflows.
• Support automated build, test, security-check, and deployment pipelines.
• Maintain deployment, environment, and operational documentation.

Reliability and Security

• Monitor service health and investigate failures or performance bottlenecks.
• Follow platform security, access control, vulnerability management, and data-handling standards.
• Support reliability, scalability, availability, and disaster-recovery practices.

Requirements & Tech Stack

• Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Electronics, or a related field.
• 0 to 2 years of relevant experience, including internships, academic projects, or personal projects.
• Programming knowledge in Python, C#, Java, JavaScript, or a similar language.
• Understanding of object-oriented programming, data structures, algorithms, HTTP, REST APIs, JSON, databases, and SQL.
• Basic familiarity with Linux command-line operations.
• Strong troubleshooting, analytical, communication, collaboration, and documentation skills.

Preferred Skills

• Exposure to Microsoft Azure.
• Familiarity with FastAPI, Flask, ASP.NET Core, Node.js, or a similar backend framework.
• Exposure to Docker, Kubernetes, GitHub Actions, or Azure DevOps.
• Exposure to Azure AI Foundry, Azure Machine Learning, Azure OpenAI, or Power Platform.
• Basic understanding of queues, caching, serverless functions, asynchronous processing, OAuth, managed identities, role-based access control, and secrets management.

Mandatory Pre-Joining Technology Training

Selected candidates must complete the assigned role-specific learning plan, hands-on exercises, knowledge checks, and readiness assessment before joining or deployment.

• Python, object-oriented programming, data structures, and algorithms
• Linux, shell fundamentals, Git, and GitHub workflows
• HTTP, HTTPS, JSON, REST APIs, webhooks, and WebSockets
• FastAPI or an equivalent backend framework
• SQL, relational databases, and basic NoSQL concepts
• Docker and container fundamentals
• Kubernetes architecture, deployments, services, configuration, security, and scaling
• Azure fundamentals, App Service, Functions, Container Apps, and Azure Kubernetes Service
• Azure AI Foundry, Azure Machine Learning, and Azure OpenAI integration
• CI/CD with GitHub Actions or Azure DevOps
• Logging, monitoring, alerts, telemetry, testing, identity, secrets, and application security

Pre-Joining Capstone

Develop an AI-enabled API, package it in Docker, apply automated tests, and deploy it through a basic CI/CD pipeline to an approved cloud or simulated environment.