Machine Learning Engineer II

5+ years of software engineering experience, with a strong focus on ML engineering and deploying machine learning models in production.
Extensive experience in full-stack development, particularly in backend environments that support AI/ML workloads.
Strong proficiency in Python, with deep expertise in LLMs, AI Agents, and ML model development.
Experience designing and deploying scalable ML systems, such as retrieval-augmented generation (RAG) pipelines and production-grade AI applications.
Extensive experience with cloud platforms (AWS, GCP, Azure) and operational best practices for ML workloads.
Familiarity with Kubernetes and other container management tools.
Ability to write well-structured, organized code and automated unit/E2E tests.
Comfortable with polyglot persistence models (SQL vs. NoSQL).
Experience with MLOps frameworks and best practices; familiarity with DevOps principles as applied to machine learning models, including model versioning, monitoring, and lifecycle management.
Ability to operate independently in unstructured environments, demonstrating a proactive and investigative approach to tackling challenges.
Excellent communication skills, with the ability to collaborate effectively in dynamic, cross-functional teams, including data scientists, researchers, and software engineers.

Build robust, scalable backend systems that support machine learning operations and data processing pipelines.
Oversee and optimize cloud infrastructure to ensure efficient deployment and operation of ML models.
Independently explore and address complex problem spaces to improve system capabilities and performance without extensive guidance.
Work closely with ML engineers and data scientists to integrate advanced ML technologies, ensuring seamless operations across various platforms.
Actively participate in research and development of new tools that can enhance our AI capabilities and workflows.

No preferred qualifications provided.