Zensar Technologies
GenAI Developer
Designs, builds, and deploys enterprise Generative AI applications for Zensar Technologies — RAG pipelines, document Q&A/chatbots, and multimodal processing using Azure OpenAI, Python (FastAPI/Flask), LangChain/LlamaIndex, FAISS, PyMuPDF, and LLM observability tools like Opik. Seeks 5–10 years software dev experience with 2–4 years in GenAI.
- Compensation
- Not disclosed
- Location
- India
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 5–10 years of software development experience with advanced Python
- 2–4 years hands-on Generative AI / 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
- REST API development and async programming
- LangChain and LlamaIndex
- Prompt engineering and LLM response optimization
- AI agents, function calling, context management, model evaluation
- Azure AI Services and Azure Storage
- Git/GitHub (Azure DevOps) and Docker
Nice to have
- LangGraph
- AutoGen / CrewAI
- Azure AI Search (Cognitive Search)
- Azure Functions
- Cosmos DB
- PostgreSQL
- Redis
- Kubernetes
- MLflow
- Hugging Face
- OCR (Azure Document Intelligence, Tesseract)
- CI/CD pipelines
- MLOps
- Transformers
Full description
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 SkillsGenerative 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.