Backend Generative AI Engineer - Associate

Why this job matters

How You’ll Contribute  

As a DevOps and AI Engineer, you’ll work as part of a team of problem solvers, designing and building AI-powered products while guiding fellow engineers on quality and standards, from strategy to execution. As a DevOps and AI Engineer, your responsibilities include but are not limited to: 

• Design, build, and ship AI-powered products — integrating machine learning models and large language models into production applications such as RAG pipelines, agentic systems, and AI-powered workflows. 
• Develop backend services in Python for model inference, prompt orchestration, and pipeline management, exposing them through RESTful APIs and microservices. 
• Participate in design discussions and architectural reviews, and uphold coding standards.
• Translate conceptual ideas and business requirements into working solutions, and evaluate new tools, frameworks, and platforms for client use cases. 
• Champion the effective and responsible use of AI-assisted coding tools to improve team productivity and quality. 
• Collaborate on CI/CD pipelines and DevOps practices (Docker containerization, automation) for reliable delivery. 
• Communicate clearly with technical and non-technical audiences, explaining complex topics in a simple way.
• Be able to conduct technical Feasibility studies from Business Ideas. 
• Stay up to date with emerging AI advancements and industry trends, and contribute to internal knowledge sharing, documentation, and reusable accelerators. 

Job Skills/Competencies Required

Experience 

• 1-3 years of professional software or AI engineering experience in an individual-contributor role.
• Demonstrated experience building and shipping AI-powered products (RAG pipelines, agentic systems, AI-powered workflows). 

Education & Certifications 

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field. 
• Relevant Azure or Microsoft certifications are a plus. 

Technical Skills & Specialized Knowledge 

• Strong proficiency in Python for backend development. 
• Experience integrating machine learning models or large language models into production applications. 
• Familiarity with AI/ML concepts and frameworks (e.g., TensorFlow, PyTorch, scikit-learn), computer vision, and popular LLM APIs (e.g., GPT, Claude, Gemini). 
• Front-end or full-stack capabilities; experience with React (Next.js) and Tailwind CSS is a plus. 
• Experience with RESTful APIs and microservices architecture. 
• Version control using Git; understanding of DevOps practices and Docker containerization. 
• Experience with cloud platforms (AWS, Azure, or GCP).