Roles & Responsibilities

• Model Fine-Tuning & Training: Fine-tune Large Language Models (LLMs) to optimize their performance for specific tasks and improve overall model accuracy.
• Model Deployment: Deploy generative AI models into production environments using cloud platforms (Azure, AWS)and containerization technologies such as Docker.
• Cloud Integration: Design and implement end-to-end AI solutions using services from Azure or AWS Marketplace, including but not limited to Cognitive Services, Object Storage, API Management (APIM), and App Services.
• NLP & Search Solutions: Implement NLP-based solutions and develop indexing/search capabilities, with in-depth experience in at least one iindexing systems.
• API Development: Build and maintain RESTful APIs using frameworks like Flask or FastAPI to serve AI models and integrate them with external systems.
• LLM Evaluation & Agentic AI:Evaluate the performance of LLMs for varioususe cases and explore the potential of agentic AI to improve system efficiency.
• Continuous Learning & Research: Stay up-to-date with the latest advancements in Generative AI, NLP, and related fields, and incorporate these advancements into the development process

Preferred Skills:

• Familiarity with other cloud-based tools and services for AI and ML development
• Knowledge of best practices for deploying AI models at scale

• Required Skills & Technologies:
• Programming Languages: Python
• Cloud Platforms: Azure, AWS
• AI Frameworks & Tools: Langchain, LangGraph, PyTorch, SpaCy, DSPy
• Web Frameworks: Flask, FastAPI
• Containerization: Docker
• Search & Indexing: Experience with at least one indexer
• Additional: MCP, API Development, LLM Evaluation, Agentic AI