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Senior Machine Learning Engineer III

Role overview

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 with hybrid search (semantic + lexical), embeddings, and search platforms

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
  • Collaborate with Data Scientists to productionize models and integrate into products

Key facts

Hard skills

Other skills

  • Collaboration
  • Communication

About the company

LexisNexis Risk Solutions logo

LexisNexis Risk Solutions

Data Analytics & Business Intelligence

LexisNexis® Risk Solutions includes seven brands that span multiple industries. Our portfolio includes the LexisNexis Risk Solutions go-to-market brand and our six Data Services brands that provide customers with innovative technologies, information-based analytics, decisioning tools and data management services that help them solve problems, make better decisions, stay compliant, reduce risk and improve operations.Our well-known brands are LexisNexis® Risk Solutions, ICIS®, Cirium®, Proagrica™, XpertHR®, EG™ and Nextens® and serve a variety of sectors including aviation, agriculture, chemical and energy, financial services, collections and payments, commercial property, corporations and non-profits, government and law enforcement agencies, healthcare, human resources, insurance and tax.We harness the power of data, sophisticated analytics platforms and technology solutions to provide insights that help businesses and governmental entities reduce risk and improve decisions to benefit people around the globe. Headquartered in metro Atlanta, Georgia, we have offices throughout the world and are part of RELX (LSE: REL/NYSE: RELX), a global provider of information-based analytics and decision tools for professional and business customers. For more information, please visit risk.lexisnexis.com and www.relx.com.Follow our brands: LexisNexis Risk Solutions: https://www.linkedin.com/company/lexisnexis-risk-solutions/ ICIS: https://www.linkedin.com/company/icisCirium: https://www.linkedin.com/company/cirium/ Proagrica: https://www.linkedin.com/company/proagrica/ XpertHR: https://www.linkedin.com/company/xperthr EG: http://www.linkedin.com/company/estatesgazette.com Nextens: https://www.linkedin.com/company/nextens/

Company details

Company typeXLarge
IndustryData Analytics & Business Intelligence
Company size10001

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Job description

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.







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