Sr. ML Engineer (MLOps)
New
J
JobgetherMachine Learning, AI
Based in the United StatesFull-TimeSenior
SalaryAnnual base salary range of $143,000–$197,000
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Job Details
- Experience
- 6+ years of experience
- Required Skills
- AWSDockerPythonKubernetesRESTful APIsMLOps
Requirements
- 6+ years of experience deploying machine learning or AI solutions in production environments.
- Strong Python programming skills and a demonstrated ability to write clean, maintainable, production-quality code.
- Experience designing and developing RESTful APIs and production backend services.
- Experience defining and working with Protobuf messages and service interfaces.
- Hands-on experience with Docker and deploying applications to Kubernetes.
- Experience working with relational databases and low-latency data stores.
- Experience using Celery or comparable technologies for distributed or asynchronous task processing.
- Strong understanding of machine learning and AI production workflows.
- Experience building retrieval-augmented generation (RAG) solutions (preferred).
- Experience setting up and maintaining vector databases (preferred).
- Production software development experience with Java or Kotlin (plus).
- Experience building and operating solutions on AWS or other cloud infrastructure (preferred).
Responsibilities
- Build and maintain the tooling, platforms, and backend services that power machine learning and generative AI solutions in production.
- Develop services supporting model training, inference, deployment, monitoring, and other stages of the ML/AI lifecycle.
- Build and maintain model evaluation metrics, testing frameworks, and supporting infrastructure.
- Develop APIs and production services that expose machine learning and AI capabilities to applications and users.
- Deploy and manage applications supporting different components of the AI/ML software development lifecycle.
- Design scalable, maintainable systems using modern software engineering and MLOps practices.
- Develop high-quality production code, primarily in Python.
- Collaborate with data scientists, analysts, and product managers to translate complex requirements into effective technical solutions.
- Take ownership of cross-functional initiatives from initial design through deployment.
- Provide technical guidance and mentorship to other team members.
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