AI Engineer, Team Lead

About the Role
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.

Core Responsibilities

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

Required Qualifications

Technical Expertise

• Agentic AI: Specialized expertise in designing and deploying Agentic AI systems and autonomous workflows.
  Languages: Mastery of Python, SQL, and TypeScript (Node.js) is required.
• AI/ML Frameworks: Extensive experience with PyTorch, TensorFlow, HuggingFace, Scikit-learn, and Pandas.
• Modern AI Patterns: Proven expertise in LLMs, RAG, similarity search algorithms, and VectorDBs.
• Agentic Frameworks: Hands-on experience with LangChain or LangGraph.
• Backend & API: Proficiency with OpenAPI, FastAPI, and building scalable microservices.
• Infrastructure & Tools: Deep knowledge of Docker, Git, CI/CD, and MLFlow.
• Cloud Platforms: Significant experience deploying AI solutions on AWS or GCP.
  
  

Experience & Education

• Industry Experience: 5+ years of professional experience in AI/ML engineering.
• Leadership Experience: 2+ years of experience managing a high-output ML team.
• Education: Bachelor’s degree or higher in Machine Learning, Artificial Intelligence, Software Engineering, or a related field.