Senior Machine Learning Engineer

Job Summary

We are seeking a Senior Machine Learning Engineer to help lead the design and validation of AI-driven product capabilities within the legal domain. This role focuses on defining what to build and why, leveraging Python and large language models (LLMs) to solve complex legal workflows. You will drive experimentation, modeling, and evaluation, partnering closely with data scientists to translate validated approaches into scalable, customer-facing solutions.

Key Responsibilities

• Design, build, and deploy scalable machine learning and generative AI solutions for legal technology products.
• Develop and optimize NLP, LLM, and Retrieval-Augmented Generation (RAG) applications for production environments.
• Engineer agentic AI workflows and multi-step reasoning systems to automate complex legal processes.
• Implement and improve retrieval systems, including vector databases, semantic search, lexical search, and hybrid search architectures.
• Build and maintain ML pipelines, APIs, model serving infrastructure, and cloud-based AI platforms.
• Establish MLOps best practices for model deployment, monitoring, observability, versioning, and lifecycle management.
• Evaluate and optimize model performance, latency, scalability, and cost efficiency across AI applications.
• Partner with product managers, legal subject matter experts, data scientists, and software engineers to deliver business-focused AI solutions.
• Troubleshoot production ML systems, identify performance bottlenecks, and implement continuous improvements.
• Provide technical leadership and mentorship on machine learning engineering, LLM architecture, and AI platform best practices.

Required Qualifications

• 7–10+ years of AI/machine learning engineering experience, including building RAG systems end-to-end and deploying solutions through production.
• Strong software and Python engineering background, with progression into machine learning engineering.
• Experience designing agentic workflows and applying generative AI techniques, including prompt engineering and end-to-end RAG system development.
• Strong experience working with unstructured data, including text and documents, document processing, NLP, embeddings, retrieval, and agentic systems.
• Strong experience creating production-grade ML systems at scale.
• Strong LLM, Generative AI, and RAG deployment experience, including implementation and end-to-end evaluation.
• Experience with the complete deployment lifecycle, including product development, production maintenance, and new feature development.
• Experience working alongside data scientists throughout the production deployment and lifecycle.
• Strong Python programming skills.