About Louisa AI
Louisa AI, a cutting-edge institutional revenue enablement platform, was originally developed at Goldman Sachs and became an independent entity in 2023. It utilizes AI to maximize revenues by mapping the expertise and relationships within organizations, primarily serving financial institutions. Louisa AI emphasizes connecting people through AI, not replacing them, leveraging relationship graphs and news integration to enhance revenue generation and connections based on expertise, relationships, and relevant information.
Responsibilities:
As the Head of AI/ML in the Louisa team, you will have the opportunity to drive innovation in Large Language Models (LLMs) and Agentic AI. This role will focus on developing autonomous AI agents where necessary, fine-tuning LLMs (Llama 2, GPT, Falcon, Mistral, etc.), and implementing scalable AI solutions usingDatabricks, cloud infrastructure, and MLOps best practices.
AI/ML Strategy & Leadership:
- Define and execute a long-term AI roadmap, focusing on Agentic AI, LLMs, and autonomous decision-
making systems. - Lead the design, training, and fine-tuning of LLMs and autonomous AI agents for real-world applications.
- Develop scalable and efficient ML pipelines on Databricks to handle large-scale AI workloads.
- Stay at the forefront of multi-agent AI, retrieval-augmented generation (RAG), and reinforcement learning from human feedback (RLHF).
Agentic AI & LLM Development:
- Build and optimize autonomous AI agents that use LLMs for planning, reasoning, and decision-making.
- Fine-tune LLMs like Llama 2, GPT, Falcon, and multi-modal AI architectures.
- Implement LLM-powered AI workflows that integrate memory, self-improvement, and goal-driven
planning.
Infrastructure & Deployment:
- Design and deploy scalable AI solutions using Databricks, Spark, and Delta Lake for efficient data
- processing.
- Implement MLOps best practices, including CI/CD pipelines, monitoring, and versioning on Databricks and cloud platforms (AWS, Azure, GCP).
- Build retrieval-augmented generation (RAG) pipelines using vector databases (Qdrant).
Team Leadership & Collaboration:
- Mentor a team of AI engineers, ML researchers, and data scientists.
- Collaborate with data engineers, software teams, and product managers to integrate AI into production
systems. - Ensure AI ethics, security, and compliance with best practices for LLMs.
Minimum Qualifications:
- Education: Bachelor’s/Master’s/Ph.D. in Computer Science, AI, Data Science, or related fields.
- Experience: 5+ years in AI/ML development, with at least 2 years leading AI teams.
- LLMs & Agentic AI: Expertise in Llama 2, GPT, Falcon, Mistral, and autonomous AI agent
frameworks. - Databricks & Scalable AI: Experience with Databricks, Apache Spark, and distributed ML training
pipelines. - AI Model Optimization: Proficiency in model pruning.
- Cloud & MLOps: Experience with Databricks, Kubernetes, Docker, and CI/CD for AI.
- Vector Databases & RAG: Hands-on experience with Qdrant and knowledge graphs.
- Strong leadership and team management skills.
- Excellent problem-solving abilities and analytical thinking.
- Ability to communicate AI concepts to both technical and non-technical stakeholders.
Extra awesome:
- Research publications or patents in Agentic AI, LLMs, or reinforcement learning.
- Comfortable multi-tasking, managing multiple stakeholders and working in a global team.
