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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer III - **Company:** LexisNexis - **Location:** Raleigh, NC, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Computer-Aided Design, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Big Data, Cloud Computing, Program Optimization, Continuous Integration, Software Debugging, DevOps, Distributed Systems, Amazon DynamoDB, Graph Database, Apache Hadoop, Python (Programming Language), Enterprise Messaging Systems, Redis, Software Engineering, Apache Solr, Data Streaming, Systems Architecture, Large Language Models, Multi-Agent Systems, Apache Spark, Caching, Cloudformation, Build Management, Containerization, Kubernetes, Information Technology, Low Latency, Apache Kafka, Graphql, Machine Learning Operations, Amazon Simple Queue Service (SQS), Terraform, Stream Processing, GPT, Data Pipelines, Docker, Jenkins, Microservices - **Published:** September 21, 2026 - **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2953247610-2 ## About the Role * Bachelor's degree in Computer Science, Engineering, or a related field. * Strong experience implementing and scaling production ML/LLM systems. * Deep experience with LLM application development, including RAG and prompt orchestration. * Strong experience designing and implementing agentic systems using agent frameworks (e.g., LangChain, LangGraph, AutoGen, Google ADK), including orchestration of multi-step workflows in production environments. * Strong experience with hybrid search (semantic + lexical), embeddings, and search platforms (e.g., Solr, OpenSearch). * Expertise in distributed systems and cloud-native development, including AWS (S3, DynamoDB). * Experience with streaming and messaging systems (e.g., Kafka, SQS) and caching (e.g., Redis). * Proficiency in Python and experience with systems languages (e.g., Rust, Go, Scala). * Experience building scalable APIs (REST/GraphQL). * Experience with containerization and orchestration (Docker, Kubernetes). * Strong software engineering fundamentals (system design, testing, CI/CD)., * Experience with LLM platforms (e.g., ChatGPT/OpenAI, Claude, Gemini, LangChain, Google ADK). * Experience with DevOps and infrastructure as code (e.g., Terraform, CloudFormation, Jenkins). * Experience with big data technologies (e.g., Spark, Hadoop). * Familiarity with graph databases (e.g., Dgraph, Neo4j, Neptune). * Experience building high-availability, low-latency systems. * Experience in legal or regulatory domains. Key Competencies * Strong system architecture and scalability mindset. * Ownership of implementation, performance, and reliability. * Ability to translate data science solutions into production systems. * Cross-functional collaboration with DS, product, and platform teams. * Excellent debugging, optimization, and operational skills. * Clear communication of technical designs and trade-offs. ## Description We are seeking a Consultant-level Machine Learning Engineer to lead the implementation and scaling of AI systems for legal products. This role focuses on how to build and scale-owning system architecture, infrastructure, and productionization of ML/LLM solutions. You will partner with Data Scientists to turn validated models and prototypes into reliable, high-performance, customer-facing systems., * Architect and implement scalable ML/LLM systems in production. * Build and deploy LLM applications, including RAG pipelines and agentic systems. * Implement hybrid search systems (semantic + lexical) using embeddings and search platforms. * Develop and maintain APIs, microservices, and model serving infrastructure. * Build data pipelines and streaming systems for large-scale data processing. * Define and develop reusable frameworks, libraries, and infrastructure for AI/ML across teams. * Optimize systems for latency, scalability, reliability, and cost efficiency. * Establish best practices for deployment, monitoring, observability, and CI/CD. * Collaborate with Data Scientists to productionize models and integrate into products. * Provide technical leadership in system design and engineering standards. ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) - [Speak, Code, Deploy: Transforming Developer Experience with Voice Commands](https://www.wearedevelopers.com/videos/1159-speak-code-deploy-transforming-developer-experience-with-voice-commands) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [13 AI Tools You Have to Try](https://www.wearedevelopers.com/magazine/219-13-ai-tools-you-have-to-try) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Who Owns Your Content in the Age of LLMs?](https://www.wearedevelopers.com/magazine/610-who-owns-your-content-in-the-age-of-llms) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)