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# Sr ML Engineer - REMOTE

- **Company:** Insight Global
- **Location:** Raleigh, NC, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Microsoft Azure, Decision-Making Software, Distributed Systems, Python (Programming Language), Lexis, Cloud Platform System, Large Language Models, Containerization, AI Platforms, Kubernetes, Machine Learning Operations
- **Published:** July 5, 2026
- **Apply:** https://www.juju.com/job/00000000gdz5vj

## About the Role

10+ years building production ML systems (not research-only)

Hands-on LLM experience in production, including:

RAG architectures

Inference performance, reliability, and monitoring

Experience designing agentic AI systems (models calling tools/APIs, multi-step workflows)

Strong distributed systems architecture experience in cloud environments (AWS, Azure, or GCP)

Kubernetes + containerization experience in production environments

Strong Python engineering background (platform-level code, not just notebooks)

Experience building or contributing to enterprise AI platforms used by multiple teams

Proven ability to lead technically (set standards, mentor engineers, influence architecture)

Comfortable working in regulated or high-reliability environments Direct experience with Model Context Protocol (MCP) servers or structured tool-calling frameworks

Deep experience with vector databases and large-scale search systems

Experience designing LLMOps / MLOps standards at the platform level

Prior work in legal, financial, healthcare, or other regulated industries

Exposure to Responsible AI governance, auditing, or compliance frameworks

Experience building internal AI platforms rather than just end-user applications

Background mentoring senior engineers or leading cross-team technical initiatives

## Description

Day to Day:

Designing and owning the architecture for enterprise AI platforms used across multiple LexisNexis products

Building and scaling LLM-powered systems (including RAG pipelines) that support legal research and decision-making tools

Designing agentic AI workflows where models reason, call tools/APIs, and execute multi-step tasks

Creating high-availability, low-latency inference systems for global, enterprise users

Establishing platform standards for model deployment, monitoring, evaluation, and reliability

Defining guardrails, permissions, and auditability for AI systems in a regulated legal environment

Working closely with product, platform, and engineering teams to ensure AI systems are reusable and scalable

Mentoring senior engineers and influencing technical direction across teams

Ensuring Responsible AI principles are embedded into system design (safety, reliability, governance)


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