About The Position

As an AI Engineer, Team Lead, you will be the technical engine coordinating AI efforts across the entire Gravyty product ecosystem. This is a high-impact "Player-Coach" role designed for a Lead Engineer who thrives on staying in the codebase—coding 70% of the time—while providing the tactical leadership and mentoring necessary to scale our AI initiatives. You will lead a high-output team to transform engagement through cutting-edge LLM applications, ensuring our suite of products provides instant, accurate, and multilingual support.

Requirements

  • Specialized expertise in designing and deploying Agentic AI systems and autonomous workflows.
  • Mastery of Python, SQL, and TypeScript (Node.js) is required.
  • Extensive experience with PyTorch, TensorFlow, HuggingFace, Scikit-learn, and Pandas.
  • Proven expertise in LLMs, RAG, similarity search algorithms, and VectorDBs.
  • Hands-on experience with LangChain or LangGraph.
  • Proficiency with OpenAPI, FastAPI, and building scalable microservices.
  • Deep knowledge of Docker, Git, CI/CD, and MLFlow.
  • Significant experience deploying AI solutions on AWS or GCP.
  • 5+ years of professional experience in AI/ML engineering.
  • 2+ years of experience managing a high-output ML team.
  • Bachelor’s degree or higher in Machine Learning, Artificial Intelligence, Software Engineering, or a related field.

Responsibilities

  • Spend most of your time architecting and writing production-ready code for core AI engagement features across Gravyty’s platforms.
  • Lead the AI team to oversee project delivery, ensuring high standards of quality, efficiency, and operational excellence.
  • Provide expert-level code reviews and mentor junior engineers to foster a culture of technical excellence within the ML team.
  • Lead the planning and coordination of AI projects across all product lines, ensuring alignment with institutional needs for recruitment, retention, and career readiness.
  • Build and optimize Retrieval-augmented generation (RAG) systems and agentic workflows to deliver personalized user support.
  • Oversee the deployment of MLOps systems to ensure the reliability and scalability of virtual assistants and predictive analytics tools.
  • Ensure all AI features comply with enterprise-grade encryption and standards such as SOC2 Type 2, FERPA, GDPR, and HIPAA.
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