AI Engineer

Job Responsibilities:

• Develop, deploy and maintain AI-enabled backend services and APIs.
• Build, deploy, and manage AI and data workflows using Databricks, including use of Databricks Asset Bundles for environment-aware deployments.
• Integrate and consume Large Language Models (LLMs) (e.g. Gemini, Claude, GPT) for use cases such as summarization, classification, retrieval-augmented generation (RAG), and conversational AI.
• Develop lightweight front-end or integration components using JavaScript to support AI-driven user experiences.
• Collaborate with product, data, and platform teams to translate business requirements into scalable AI solutions.
• Support model integration, API testing, logging, monitoring, and performance optimization.
• Follow secure coding practices and contribute to documentation to support governance, handover, and audit readiness.

Job Requirements:

• Hands-on experience with Python for backend and AI-related development.
  
• Experience working with Databricks, including notebooks, jobs, and deployment using Databricks Asset Bundles.
  
• Practical experience integrating and utilizing Large Language Models (LLMs) such as Gemini, Claude, or GPT via APIs.
  
• Experience building RESTful APIs using FastAPI or similar Python frameworks.
  
• Working knowledge of JavaScript for UI integration or service consumption.
  
• Familiarity with cloud-based AI/data architectures and API-driven systems.
  
• Solid understanding of machine learning and AI concepts, including model inference, prompting, embeddings, and evaluation techniques.
  
• Experience across the AI development lifecycle, including development, testing, deployment, and monitoring.
  
• Strong analytical, troubleshooting, and debugging skills.
  
• Ability to write clean, maintainable, and well-documented code.
  
• Experience working in Agile/Scrum delivery environments.
  
• Good communication and collaboration skills, with the ability to work effectively with cross-functional teams.
  
• Awareness of model risk, data privacy, and responsible AI practices and considerations.