Principal Gen AI Engineer

Role Overview: As a Principal GenAI/AI Engineer and Architect, you will be joining an innovative team with a focus on Generative AI and Agentic AI. Your role will require over a decade of hands-on experience in the AI/GenAI space, where you will bridge the gap between AI research and practical, enterprise-grade applications. Your deep technical expertise and architectural vision will be crucial in architecting, developing, and deploying cutting-edge GenAI solutions. Key Responsibilities: - Lead the design and implementation of scalable, reliable, and secure GenAI and Agentic AI systems. This includes architecting GenAI pipelines for data ingestion, model training, and production deployment. - Design and build autonomous AI agents that can perform complex, multi-step tasks using advanced agentic frameworks like LangChain and LangGraph. - Possess practical knowledge of integrating Large Language Models (LLMs) and Small Language Models (SLMs) to optimize for cost, performance, and efficiency. - Develop, train, and fine-tune large language models (LLMs) and other generative models, understanding concepts like Retrieval-Augmented Generation (RAG) and vector databases. - Drive hands-on development of .NET-based applications and projects, applying strong software engineering principles for robust, maintainable, and efficient systems. - Continuously research and analyze the latest AI and GenAI tools, platforms, and models to provide expert recommendations for solving specific business problems. - Provide technical leadership and mentorship to junior engineers, helping them grow their skills in GenAI and AI-assisted development tools. - Work closely with cross-functional teams to translate business needs into technical GenAI solutions. Qualifications Required: - 10+ years of professional experience in software engineering, with at least 5 years specifically in AI/GenAI. - Deep expertise in Python and proficiency with major GenAI frameworks such as PyTorch, TensorFlow, or Hugging Face. - Hands-on expertise in the Microsoft .NET technology stack (e.g., C#, ASP.NET Core) and software engineering best practices. - Proficiency in designing and building cloud-native applications on major cloud platforms with expertise in microservices, containerization (Docker, Kubernetes), and serverless architectures. - Strong experience with LangChain and LangGraph, as well as practical knowledge on SLM and LLM integration. - Capability in architecting complex GenAI systems that are scalable, maintainable, and cost-efficient. - Solid understanding of AI/GenAI concepts, including LLMs, RAG, and fine-tuning. - Experience with cloud platforms such as Azure, AWS, or GCP. - Excellent problem-solving, analytical, and communication skills, with a passion for staying updated on new technologies in the Generative AI space. (Note: Preferred qualifications and any additional details of the company were not provided in the job description.),