Job description

Are you looking to develop your Machine Learning Engineer career? Do you enjoy coaching others to achieve high standards? This is a full-time position based in Raleigh, NC . (Hybrid - 3 days in office) 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. #AIFluent 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. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120. Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here . Please read our Candidate Privacy Policy . We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. USA Job Seekers: EEO Know Your Rights .

Requirements

Must-haves

Are you looking to develop your Machine Learning Engineer career?
Do you enjoy coaching others to achieve high standards?
This is a full-time position based in Raleigh, NC .
(Hybrid - 3 days in office)
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.

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlllmci/cdawspythonrustgorestgraphqldockerkubernetes

Platform: workday
Effort: long