Generative AI Leader

Summary

Join Tiger Analytics as an Engineering Lead specializing in Generative AI (GenAI) and Large Language Models (LLM)! Lead the design, development, and integration of AI-powered components into real-world applications. Collaborate with AI/ML teams to operationalize models and own full-stack development and integration of GenAI features. Establish best practices for scalable and secure AI application development, optimizing performance and reliability. Mentor engineers and guide code reviews, architectural decisions, and DevOps practices. Evaluate emerging GenAI tools and make build-vs-buy recommendations. Oversee application-level development, testing, and deployment. This role requires strong engineering leadership and a practical approach to application development.

Requirements

• 10+ years of full-stack application engineering experience, with at least 2 years leading cross-functional teams
• Proven track record developing and integrating GenAI/LLM-based features into enterprise applications
• Exposure to LLM fine-tuning, RAG pipelines, or model evaluation techniques for NLP use cases
• Familiarity with LLM orchestration tools (LangChain, Semantic Kernel etc) and embedding/vector store technologies
• Proficiency in application frameworks such as Node.js, React, Python, or Java Spring Boot
• Strong understanding of API development, front-end/backend integration, and cloud services (AWS, Azure, or GCP)
• Hands-on experience with containerization (Docker, Kubernetes) and CI/CD pipelines
• Strong understanding of application-level security, performance tuning, and production monitoring

Responsibilities

• Lead the architecture, design, and implementation of GenAI/LLM-based solutions into real-world enterprise-ready applications
• Collaborate with AI/ML teams to operationalize models using APIs, embeddings, vector databases, and prompt engineering techniques
• Own full-stack development and integration of GenAI features into web/mobile applications
• Establish best practices for scalable, secure, and maintainable AI-powered application development
• Optimize application performance, latency, and reliability of AI features in production
• Drive DevOps practices for continuous delivery and monitoring of AI-enabled services in production
• Mentor engineers and guide code reviews, architectural decisions, and DevOps practices
• Guide engineering teams in code quality, architectural reviews, and technical mentoring
• Evaluate emerging GenAI tools and LLM frameworks (OpenAI, LangChain etc.) and make build-vs-buy recommendations
• Oversee application-level development, testing, and deployment

Preferred Qualifications

Deep knowledge of healthcare payer workflows (claims processing, member portals, benefit management, etc)