Lead Production ML/LLM Engineer (Hybrid)

LexisNexis; Raleigh (NC); Hybrid; USD 118,000 - 220,000; Full time

Job summary

LexisNexis is seeking a Consultant-level Machine Learning Engineer in Raleigh, NC (hybrid—3 days in office). You will own architecture, scaling, and productionization of ML/LLM solutions for legal products, working with DS to turn prototypes into customer-facing systems.

You’ll design RAG pipelines, agentic workflows, and hybrid search, while building scalable APIs and data pipelines. Strong Python, cloud, and distributed systems skills are essential.

Qualifications

• Experience building and scaling production ML/LLM systems.
• Deep expertise in LLM applications, including RAG and prompt orchestration.
• Experience designing agentic systems with established frameworks.

Responsibilities

• Architect and deploy scalable ML/LLM systems in production.
• Build and maintain LLM apps, including RAG pipelines and agents.
• Develop hybrid search solutions (semantic + lexical) with embeddings.
• Create APIs, microservices and model-serving infrastructure.
• Build data pipelines and streaming systems for large-scale data.
• Develop reusable AI/ML infrastructure and libraries.
• Ensure latency, reliability, and cost efficiency; enable CI/CD and observability.

Skills

LLM development

RAG pipelines

Prompt orchestration

LangChain

LangGraph

AutoGen

Google ADK

Distributed systems

Python

REST/GraphQL

Docker

Kubernetes

AWS

Embeddings

Education

Bachelor's degree in Computer Science, Engineering, or a related field

Tools

LangChain

LangGraph

AutoGen

Google ADK

Solr/OpenSearch

Kafka

SQS

Redis

Docker

Kubernetes

Terraform

CloudFormation

Jenkins

Spark

Neo4j

Dgraph

Neptune

S3

DynamoDB

Job description

LexisNexis is seeking a Consultant-level Machine Learning Engineer in Raleigh, NC (hybrid—3 days in office). You will own architecture, scaling, and productionization of ML/LLM solutions for legal products, working with DS to turn prototypes into customer-facing systems.

You’ll design RAG pipelines, agentic workflows, and hybrid search, while building scalable APIs and data pipelines. Strong Python, cloud, and distributed systems skills are essential.