Senior ML Engineer

Job Description

Join a global leader in legal, regulatory, and business information as a Senior Machine Learning Engineer supporting a next-generation AI Platform team. This is a 6-12 month remote contract-to-hire opportunity. You'll help build and scale enterprise AI platforms leveraging LLMs, RAG architectures, agentic AI frameworks, distributed ML systems, Kubernetes, Python, and cloud technologies that power mission-critical applications used around the world.

This is a rare opportunity to influence the future of enterprise AI at scale. The team is building the foundational infrastructure behind advanced AI products and is looking for someone who can architect complex AI systems, establish engineering standards, and guide the evolution of agentic workflows across the organization.

Contract Duration: 6-12 Months (Contract-to-Hire)

Required Skills & Experience
PhD in a relevant field or 8+ years of experience with a Master's degree or 12+ years of experience with a Bachelor's degree
8+ years building production-scale machine learning systems
Extensive experience with LLMs, Generative AI, and RAG architectures
Strong Python development background
Experience architecting distributed systems in AWS, Azure, or Google Cloud Platform
Hands-on experience with Kubernetes, containerization, and scalable inference platforms
Experience developing agentic AI systems and tool orchestration frameworks
Experience implementing or managing MCP servers and structured tool-calling architectures
Deep understanding of MLOps, observability, reliability, and model evaluation
Strong system design and architecture experience

Desired Skills & Experience
Experience building enterprise AI platforms at scale
Background with vector databases and semantic search technologies
Responsible AI and governance experience
Proven ability to influence cross-functional technical direction
Experience mentoring senior engineers
Strong communication and stakeholder management skills
Experience operating within highly regulated or enterprise environments
What You Will Be Doing
Tech Breakdown
40% LLMs, RAG, and Agentic AI Systems
30% Distributed Systems & Cloud Architecture (AWS/Azure/Google Cloud Platform)
20% MLOps, Platform Engineering & Reliability
10% Technical Leadership & Mentorship

Daily Responsibilities
80% Hands On
5% Management Duties
15% Team Collaboration

You'll design scalable AI platform architecture, build enterprise-grade agent systems, define standards for tool orchestration and MCP integration, establish AI governance and security controls, improve ML platform reliability, and partner with engineering leaders to shape the long-term AI strategy across the organization.