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
ok
ok