About The Position

"Navitas Partners, LLC" is seeking an experienced Principal GenAI Engineer to lead the design, development, and deployment of advanced generative AI solutions. This role requires deep expertise in large language models (LLMs), strong hands-on engineering skills, and the ability to collaborate across technical and business teams to deliver scalable, high-impact AI products.

Requirements

  • 10+ years of professional experience building machine learning models and production-grade ML systems.
  • 3+ years of hands-on experience with LLMs and generative AI techniques, including prompt engineering, RAG, and agent-based systems.
  • Expert-level proficiency in Python, LangChain/LangGraph, and SQL.
  • Strong understanding of cloud services and architectures on Azure, AWS, or GCP for deploying and scaling GenAI applications.
  • Excellent communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.

Responsibilities

  • LLM Solution Development: Lead the design, training, fine-tuning, and deployment of LLM-based solutions using techniques such as prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
  • Codebase Ownership: Develop and maintain high-quality, scalable, and reusable code using Python (including frameworks such as LangChain and LangGraph) and SQL, following performance and best-practice standards.
  • Cloud Integration & Deployment: Support the deployment of generative AI applications on cloud platforms such as Azure, AWS, or GCP, with attention to resource optimization, reliability, and CI/CD pipelines.
  • Cross-Functional Collaboration: Partner with product owners, data scientists, and business subject matter experts to gather requirements, translate business needs into technical solutions, and deliver production-ready AI capabilities.
  • Technical Leadership & Mentorship: Provide technical guidance, mentorship, and knowledge sharing to engineering teams, promoting best practices in machine learning and LLM development.
  • Continuous Innovation: Stay current with the latest advancements in generative AI and LLM research, evaluating and experimenting with emerging techniques to improve model performance and solution effectiveness.
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