AI Engineer, Team Lead
Role summary
Gravyty is seeking an AI Engineer, Team Lead for a high-impact "Player-Coach" role. This position requires spending approximately 70% of the time coding and 30% on technical leadership and mentoring. The successful candidate will lead the AI team to transform engagement through cutting-edge LLM applications, focusing on RAG systems and agentic workflows. Responsibilities include architecting and writing production code, overseeing project delivery, mentoring junior engineers, coordinating AI projects across product lines, and ensuring AI features comply with enterprise-grade security and privacy standards (SOC2, FERPA, GDPR, HIPAA). The role demands mastery of Python, SQL, TypeScript, AI/ML frameworks, modern AI patterns, agentic frameworks like LangChain/LangGraph, backend technologies, and cloud platforms (AWS/GCP). A minimum of 5 years in AI/ML engineering and 2 years of management experience is required.
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.