About the Role:
We are seeking an experienced Lead AI Engineer with a strong background in Azure-based AI solutions. The ideal candidate should have hands-on experience working with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agent development. Proficiency in Python toolkits is highly preferred. This role offers an opportunity to work on cutting-edge AI projects, leveraging state-of-the-art tools to build intelligent and scalable solutions.
Key Responsibilities:
- Design, develop, and deploy AI solutions leveraging Azure AI services.
- Implement LLM-powered applications, fine-tuning models for specific use cases.
- Develop Retrieval-Augmented Generation (RAG) workflows to enhance AI-based search and decision-making.
- Build and optimize AI agents for automation, recommendation, and conversational AI.
- Utilize Python toolkits for AI model development, testing, and deployment.
- Work with cross-functional teams to integrate AI solutions into existing platforms.
- Ensure scalability, efficiency, and reliability of AI models and pipelines.
- Stay up to date with advancements in AI, machine learning, and cloud computing.
Required Skills & Experience:
- 5+ years of experience in AI/ML engineering.
- Hands-on expertise with Azure AI services (e.g., Azure OpenAI, Azure Machine Learning, Cognitive Services).
- Proven experience in working with LLMs, including fine-tuning and prompt engineering.
- Strong knowledge of RAG techniques and vector search implementation.
- Experience in designing and deploying AI agents.
- Proficiency in Python and its AI/ML-related libraries (e.g., TensorFlow, PyTorch, LangChain, Hugging Face, FastAPI).
- Experience with Vector Databases (e.g., Pinecone, FAISS, Weaviate) and GraphQL (preferred).
- Familiarity with MLOps practices, CI/CD for AI models, and cloud-based deployment.
- Strong problem-solving skills and ability to work in a collaborative environment.
Nice-to-Have:
- Experience with Kubernetes, Docker, and cloud-native AI solutions.
- Understanding of Natural Language Processing (NLP) and Knowledge Graphs.
- Background in Reinforcement Learning with Human Feedback (RLHF).
- Previous experience working with enterprise AI applications.
