Generative AI Engineer

Roles & Responsibilities

Design & Develop:

• Architect and implement Generative AI applications using Azure OpenAI Services, Azure Machine Learning, and other Azure AI tools.
• Develop and fine-tune LLMs for domain-specific tasks using frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, etc.
• Build agentic AI systems capable of autonomous reasoning and task execution.

NLP and Advanced Language Models:

• Work with NLP pipelines (text classification, summarization, entity extraction, question answering).

MLOps & Azure Integration:

• Utilize CI/CD pipelines, model versioning, deployment, and monitoring for AI models using Azure Machine Learning MLOps capabilities.
• Collaborate with DevOps to integrate AI models into production systems.

Innovation & Scalability:

• Stay updated on AI advancements—especially LLMs and Generative AI—to improve AI solutions.
• Promote best practices for scalable and secure AI system development on Azure.

Mentorship & Collaboration:

• Provide technical mentorship to junior engineers; share knowledge within the team.
• Work closely with cross-functional teams (Product, Engineering) to translate requirements into technical solutions.

Requirements

Experience:

• Minimum 4 years in AI engineering, focused on Generative AI, LLMs, and NLP.

• Proven experience building and deploying AI models with Azure AI tools: Azure OpenAI, Azure ML, Azure Cognitive Services.
• Strong use of LLM frameworks: LangChain, LlamaIndex, Hugging Face, etc.
• Hands-on experience with MLOps pipelines and CI/CD.

Technical Skills:

• Experience with backend development, especially building APIs or integrating AI models into web apps.

Soft Skills:

• Excellent problem-solving; focus on production-ready solutions.
• Strong communication and collaboration; able to explain complex AI topics to non-technical stakeholders.