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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Job description provided by Zensar.
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