About Slice

Issue equity to your teams anywhere, stay in compliance locally, optimize tax for your employees, and do it all easily & quickly.

Slice is a Global Equity Management platform for multinational companies. It handles the full equity lifecycle — from correctly setting up option plans around the globe to awarding grants and liquidity — while keeping companies and employees in compliance with each country’s local equity laws and away from tax penalties. Slice covers 40+ countries, including the US, UK, Australia and Israel, and serves startups from seed to growth stage, plus several unicorns. You can read more about Slice here: https://www.sliceglobal.com/

Role

As the Lead AI Engineer at Slice, you will lead developing the infrastructure and pipelines to power AI-driven solutions for legal, equity, and tax document management. You will be responsible for building and optimizing production-level machine learning systems and AI multi agent systems, with a focus on scalability, reliability, and performance. This is an exciting opportunity for engineers passionate about applying AI cutting edge technology in a rapidly growing startup, particularly within the fintech and legal tech industries.

Key Responsibilities

• AI System Development: Lead the design, implementation, and deployment of scalable ML models and LLM-based agents for document analysis and intelligent automation.
• Agent & Multi-Agent Systems: Build and orchestrate production-grade AI agents and multi-agent systems to handle complex workflows using frameworks like LangGraph or AutoGen.
• ML Pipeline & Infrastructure: Own end-to-end ML pipelines and infrastructure, ensuring efficient data ingestion, model training, and deployment on cloud platforms (GCP, Firebase).
• Cross-Functional Integration: Collaborate with backend and DevOps teams to integrate AI solutions into the platform, aligning with technical and product goals.
• Monitoring & Optimization: Set up monitoring for model/agent performance, implement feedback loops, and optimize for robustness and reliability in production.
• Innovation & Research Integration: Stay current with AI advancements and integrate modern tools and practices to enhance system performance and maintainability.

Qualifications

• Experience: 5+ years of experience building and deploying machine learning systems in production, with a focus on infrastructure, scalability, and reliability. Proven track record leading ML or AI initiatives end-to-end.
• AI & Agent Systems: Expertise in ML pipelines and lifecycle management, with hands-on experience developing LLM-based agents, RAG pipelines, and/or multi-agent systems for production use including monitoring, tracing and evaluations.
• Programming: Proficient in Python (Node.js a plus), with deep experience in ML frameworks (e.g., PyTorch, TensorFlow) and agent/LLM tooling (e.g., LangChain, LangGraph, AutoGen).
• Cloud & Infrastructure: Strong knowledge of deploying AI workloads (GCP and Firebase a plus), and experience with containerization, and CI/CD for AI.
• Problem Solving: Ability to independently drive complex projects, optimize infrastructure, and deliver robust AI systems in dynamic startup environments.
• Leadership & Collaboration: Strong communication and cross-functional collaboration skills. Experience mentoring engineers and aligning AI work with product and platform teams.