Senior Machine Learning Engineer III Raleigh, NC

Remitly•Raleigh, NC

1d•Hybrid

About The Position

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.

Requirements

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).

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.

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.