TQUKE0624_4448 - AI Engineer

AI Engineer with a strong background in Azure-based AI solutions. 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:


· 3+ 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.

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