Senior Machine Learning Engineer III

LexisNexis

Seattle, WA, United StatesFull-time$118,300–$219,800Posted Oct 23, 2026

About the role

Are you looking to develop your Machine Learning Engineer career?

Do you enjoy coaching others to achieve high standards?

About the Role

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.

Key Responsibilities

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.

Required Qualifications

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).

Preferred Qualifications

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.

U.S. National Base Pay Range: $118,300 - $219,800. Geographic differentials may apply in some locations to better reflect local market rates. This job is eligible for an annual incentive bonus.

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

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Responsibilities

  • Architect, implement, and scale production ML and LLM systems.
  • Build LLM applications, RAG pipelines, agentic systems, and hybrid search.
  • Develop APIs, microservices, model-serving infrastructure, and data pipelines.
  • Create reusable AI/ML frameworks and infrastructure across teams.
  • Optimize systems for latency, scalability, reliability, and cost.
  • Establish deployment, monitoring, observability, and CI/CD practices.
  • Partner with Data Scientists to productionize models and integrate them into products.
  • Provide technical leadership in system design and engineering standards.

Qualifications

  • Bachelor’s degree in Computer Science, Engineering, or a related field.
  • Strong experience implementing and scaling production ML and LLM systems.
  • Deep experience building LLM applications, RAG, prompt orchestration, and production agentic workflows.
  • Experience with hybrid search, embeddings, and search platforms such as Solr or OpenSearch.
  • Expertise in distributed systems, cloud-native development, and AWS services such as S3 and DynamoDB.
  • Experience with streaming and messaging systems, caching, scalable APIs, Docker, and Kubernetes.
  • Proficiency in Python and experience with a systems language such as Rust, Go, or Scala.
  • Strong software engineering fundamentals, including system design, testing, and CI/CD.

Benefits

  • Annual incentive bonus eligibility
  • Country-specific benefits

Skills mentioned

PythonMachine LearningLarge Language ModelsRetrieval-Augmented GenerationAI AgentsLangChainDistributed SystemsAWSKubernetesREST APIs

About LexisNexis

LexisNexis Legal & Professional is a leading global provider of legal, regulatory and business information and analytics that help customers increase productivity, improve decision-making and outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® and Nexis® services. LexisNexis Legal & Professional, which serves customers in more than 150 countries with 10,600 employees worldwide, is part of RELX, a global provider of information-based analytics and decision tools for professional and business customers.

Law Practice501-1,000 employeesLondon

H-1B sponsorship history

Historical employer filing data was found for LexisNexis. The employer record includes 393 historical certified applications. This is employer-level history, not a guarantee that this role currently offers sponsorship.