GenAI Developer

Key Responsibilities

• Design, develop, and deploy enterprise Generative AI applications using Azure OpenAI Services.
• Build Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.
• Develop scalable REST APIs using FastAPI and Flask.
• Integrate Vision LLMs for image, document, and multimodal understanding.
• Build document processing pipelines using PyMuPDF for PDF extraction, parsing, and preprocessing.
• Implement semantic search using FAISS Vector Database.
• Engineer prompts and optimize LLM responses for enterprise use cases.
• Develop AI-powered chatbots, document Q&A, summarization, and intelligent automation solutions.
• Optimize AI models for latency, scalability, and cost efficiency.
• Integrate AI solutions with enterprise applications and cloud services.
• Implement monitoring, evaluation, and experimentation frameworks using Opik or similar LLM observability tools.
• Collaborate with product managers, architects, data scientists, and software engineers to deliver AI solutions.
• Ensure AI applications follow security, governance, and responsible AI best practices.

Required Skills

Generative AI

• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings
• Semantic Search
• AI Agents
• Function Calling
• Context Management
• Model Evaluation

Cloud & AI Platforms

• Azure OpenAI Service
• Azure AI Services
• Azure Cognitive Search (preferred)
• Azure Storage
• Azure Functions (preferred)

Programming

• Python (Advanced)
• FastAPI
• Flask
• REST API Development
• Async Programming

AI Frameworks & Libraries

• LangChain
• LlamaIndex
• PyMuPDF
• FAISS Vector Database
• Vision LLMs
• OpenAI SDK
• Transformers (preferred)

Development Tools

• Visual Studio Code (VS Code)
• PyCharm
• Git
• GitHub/Azure DevOps
• Docker

Observability & Evaluation

• Opik
• Prompt evaluation
• LLM monitoring
• Experiment tracking
• Performance benchmarking

Required Experience

• 5–10 years of software development experience with strong Python expertise.
• Minimum 2–4 years of hands-on experience in Generative AI and LLM-based application development.
• Experience implementing enterprise RAG architectures.
• Strong experience with Azure OpenAI.
• Experience integrating Vision LLMs for document and image processing.
• Hands-on experience with vector databases such as FAISS.
• Experience building production-ready AI APIs using FastAPI or Flask.
• Experience processing large PDF/document repositories using PyMuPDF.
• Experience with AI evaluation and observability tools such as Opik.
• Experience deploying AI applications in cloud environments.

Nice-to-Have Skills

• LangGraph
• AutoGen/CrewAI
• Azure AI Search
• Cosmos DB
• PostgreSQL
• Redis
• Kubernetes
• MLflow
• Hugging Face
• OCR (Azure Document Intelligence, Tesseract)
• CI/CD pipelines
• MLOps

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