Generative AI & AI Engineering

• Design, develop, and deploy enterprise Generative AI applications using Azure OpenAI Services.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.
• Develop AI-powered chatbots, document Q&A systems, summarization engines, and intelligent automation solutions.
• Implement semantic search solutions using vector databases such as FAISS and Azure AI Search.
• Engineer prompts and optimize LLM performance for enterprise use cases.
• Build AI Agents and agentic workflows using modern orchestration frameworks.
• Integrate Vision LLMs for document understanding, image analysis, OCR, and multimodal AI applications.
• Develop document processing pipelines using PyMuPDF and OCR technologies.
• Implement model evaluation, prompt testing, experimentation, and observability frameworks.
• Optimize AI models and services for scalability, latency, reliability, and cost efficiency.

Python & Backend Engineering

• Design, develop, and maintain scalable Python applications and services.
• Build RESTful APIs and microservices using FastAPI, Flask, and Django.
• Write clean, reusable, maintainable, and well-documented code.
• Implement multithreaded and asynchronous applications for high-performance workloads.
• Integrate applications with databases, cloud services, AI platforms, and third-party APIs.
• Troubleshoot production issues and perform root-cause analysis.
• Develop automated unit tests, integration tests, and performance tests.
• Participate in code reviews and mentor junior developers.
• Build secure, reliable, and scalable software architectures.

Cloud, DevOps & Deployment

• Deploy AI and backend applications on Azure, AWS, or GCP.
• Implement containerized deployments using Docker and Kubernetes.
• Build CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools.
• Implement monitoring, logging, alerting, and performance benchmarking.
• Ensure compliance with security, governance, and Responsible AI practices.
• Collaborate with architects, product managers, data scientists, and engineering teams.

Generative AI

• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Embeddings
• Semantic Search
• AI Agents / Agentic Workflows
• Function Calling
• Context Management
• Model Evaluation
• Prompt Optimization
• LLM Monitoring & Observability

AI Platforms & Cloud Services

• Azure OpenAI Service
• Azure AI Services
• Azure AI Search / Cognitive Search
• Azure Storage
• Azure Functions

Programming & Backend Development

• Python (Advanced)
• Object-Oriented Programming (OOP)
• Data Structures & Algorithms
• FastAPI
• Flask
• REST API Development
• OpenAPI / Swagger
• JSON
• OAuth2 / JWT Authentication
• Async Programming
• Multithreading

AI Frameworks & Libraries

• LangChain
• LlamaIndex
• LangGraph
• OpenAI SDK
• PyMuPDF
• FAISS Vector Database
• Vision LLMs

Databases

• PostgreSQL
• Oracle
• Redis

Messaging & Event Processing

• RabbitMQ
• Apache Kafka
• Redis Cache

DevOps & Infrastructure

• Docker
• Kubernetes
• Git
• GitHub
• Azure DevOps
• CI/CD Pipelines

Evaluation & Observability

• Opik
• Evaluation
• Experiment Tracking
• LLM Monitoring
• Performance Benchmarking

Document Intelligence

• Azure Document Intelligence
• OCR Technologies
• Tesseract OCR
• PDF Processing
• Document Parsing & Extraction

Required Experience

• 5 to 10 years of software development experience with strong Python expertise.
• 2 to 3 years of hands-on experience in Generative AI and LLM-based application development.
• Strong experience developing scalable REST APIs using FastAPI, Flask, or Django.
• Experience designing and implementing enterprise RAG architectures.
• Hands-on experience with Azure OpenAI Service and AI solution deployment.
• Experience with Vector Databases such as FAISS and Azure AI Search.
• Experience integrating Vision LLMs, OCR, and document intelligence solutions.
• Experience with relational and NoSQL databases.
• Experience building microservices and distributed systems.
• Experience deploying applications using Docker and Kubernetes.
• Experience implementing CI/CD pipelines and DevOps practices.
• Experience with AI observability, evaluation, and monitoring tools.
• Strong debugging, performance optimization, and troubleshooting skills.
• Experience working in Agile/Scrum delivery environments.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field.
• Experience with Agentic AI architectures and multi-agent systems.
• Experience in MLOps and AI Governance.
• Experience building enterprise knowledge management and document intelligence platforms.