Director of AI

The Director of AI leads the design, development, and delivery of AI-powered capabilities across Florence products. This is a hands-on technical leadership role responsible for guiding architecture, mentoring engineers, evaluating emerging AI technologies, and partnering closely with engineering teams to deliver scalable, production-ready AI solutions. While this role includes people leadership, success is measured by the ability to help teams solve complex technical challenges and accelerate the delivery of AI capabilities. 

• Lead the technical design and architecture of AI-powered products and platforms.
• Evaluate and recommend LLMs, AI frameworks, orchestration platforms, and emerging AI technologies.
• Remain hands-on by building prototypes, validating technical approaches, and helping teams solve complex AI engineering challenges.
• Review architecture, code, and technical designs to ensure scalable, secure, and maintainable solutions.
• Guide engineers on best practices for Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), prompt engineering, and model integration.
• Mentor AI engineers through technical coaching, design reviews, and pair problem-solving.

• Lead the development and operationalization of machine learning pipelines, including data preparation, feature engineering, model training, validation, deployment, monitoring, and continuous improvement.
• Drive the best practices and adoption of MLOps practices to enable repeatable, scalable, and reliable machine learning model development and deployment across the organization.
• Work alongside engineering teams to unblock technical challenges and accelerate delivery.
• Partner with Product Management to define and implement AI capabilities that solve customer problems.
• Ensure AI solutions are reliable, observable, performant, cost-efficient and production-ready.
• Balance rapid experimentation with engineering quality and operational excellence.

• Design and Enhance Florence's AI platform, including machine learning pipelines , LLM/model orchestration, vector search, Agentic AI frameworks, Model Context Protocol (MCP), AI gateways, Knowledge retrieval systems, evaluation pipelines, feature stores, model serving infrastructur  and observability.
• Establish AI Development Lifecycle (AI DLC) practices, including prompt engineering, evaluation, testing, deployment, monitoring, and governance.
• Establish engineering standards and reusable patterns that enable teams to deliver AI solutions consistently.
• Continuously evaluate new AI tools and frameworks to improve developer productivity and product capabilities.

• Lead, mentor, and grow a team of AI Engineers and Machine Learning Engineers.
• Build engineering capabilities across Generative AI, classical Machine Learning, MLOps, and AI platform engineering 
• Provide day-to-day technical guidance and engineering leadership.
• Foster collaboration, experimentation, and continuous learning across the team.
• Help engineers develop expertise in modern AI technologies and engineering practices.

• Partner with Product Management on AI roadmaps and prioritization.
• Work closely with Platform/ Product Engineering, Security, DevOps, QA, and Data Engineering teams.
• Partner closely with Data Engineering and Data Science teams to establish scalable data pipelines, feature engineering practices, and production machine learning workflows. 
• Collaborate with Clinical, Customer Success, and Product teams to deliver impactful AI solutions.
• Contribute to engineering planning and technical roadmaps.
• Work closely with Engineering leaders to prioritize AI initiatives and remove delivery risks.

• Ensure AI systems are secure, reliable, and compliant.
• Implement guardrails, evaluation frameworks, and responsible AI engineering practices.
• Partner with Security and Compliance teams on regulated AI deployments.
• Establish engineering standards for safe AI adoption.

An Ideal Candidate Has:

• 8+ years of software engineering experience, including significant experience designing, building, deploying, and operating production AI and machine learning systems. 
• 4+ years leading engineering teams in a technical leadership capacity.
• Strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, prompt engineering, and modern AI application architectures.
• Experience building and scaling production AI systems, machine learning pipelines, and MLOps platforms in cloud-native environments. .
• Demonstrated ability to evaluate new AI technologies and translate them into practical engineering solutions.
• Strong understanding of production machine learning engineering practices, including model performance monitoring, drift detection, experiment tracking, model versioning, and continuous delivery of ML models. 
• Proven experience leading architecture discussions, mentoring technical teams, and influencing engineering direction.
• Excellent communication skills with the ability to engage effectively with executives, product leaders, and engineering teams.

We’ll Be Extra Excited If You Have:

Experience with: Amazon Bedrock,  AWS SageMaker, AWS AgentCore, Claude,TensorFlow or PyTorch, Feature Stores, ML Pipeline orchestration tools, OpenAI, Gemini, LangGraph, LangChain, MCP (Model Context Protocol), Kafka, Snowflake, Kubernetes, Docker, Python, MLflow, Vector databases (Pinecone, pgvector, OpenSearch), Healthcare or regulated SaaS environments

Hands-on Technical Expectations: 

• Stay current with advances in Generative AI and AI engineering.
• Build proof-of-concepts to evaluate new technologies when appropriate.
• Participate in architecture reviews and technical design sessions.
• Guide engineers through complex implementation challenges.
• Contribute to prototypes or reference implementations for strategic initiatives.