Role overview
- Actively contribute to AI/MLOps development within assigned product team
- Implement platform capabilities and patterns effectively
- Write high-quality, maintainable code following team standards
- Develop and maintain ML systems using platform capabilities
- Implement robust CI/CD pipelines for ML models
- Ensure proper testing and validation of ML systems
- Balance team-specific needs with platform standardization
- Champion platform adoption within a team
- Follow and help refine platform patterns and best practices
- Identify opportunities for leveraging platform capabilities
- Provide feedback on platform features and usability
- Help validate platform patterns through direct implementation
- Share knowledge and experiences with other MLEs
- Contribute to platform documentation and examples
- Participate in code reviews with focus on platform patterns
- Implement and maintain ML pipelines using platform tools
- Document technical decisions and implementations
- Follow established best practices and standards
- Contribute to technical discussions and design reviews
- Work closely with product team to understand AI development needs
- Provide implementation feedback to platform team
- Participate in technical discussions and knowledge sharing
- Help identify opportunities for platform improvement
What we're looking for
- Strong technical background in ML engineering with demonstrated coding expertise - Required
- Strong Python development expertise with focus on ML systems and AI/MLOps - Required
- Familiarity of ML workflows and operational requirements - Required
- Hands-on experience implementing model CI/CD pipelines - Required
- Experience with modern Python development practices including type checking, testing frameworks, and package management - Required
- History of successful collaboration with product teams - Required
- Familiarity with ML Development Lifecycle management and MLOps best practices - Required
- Familiarity of ML Monitoring and observability - Required
- Experience with LLMs and Infrastructure - Preferred
- Familiarity integrating with feature stores, feature caches and model serving platforms - Preferred
- Understanding of ML/AI platform tooling and patterns - Preferred
- Familiarity with Distributed model training - Preferred
- Hands-on experience with Kubeflow, Argo, MLFlow or other ML/AI Training orchestrators - Preferred
- Familiarity of ML/AI metadata tools and model registries – Preferred
- Hands-on experience with Terraform or other IaC tools - Preferred
- Hands on experience building ML/AI solutions on GCP and Vertex AI - Preferred
- Familiarity with ML model lifecycle and common ML libraries (PyTorch, scikit-learn, XGBoost, AutoGluon, CatBoost, TensorFlow, Keras) - Preferred
- Familiarity with major LLM Models (Gemini, Cluade, ChatGPT, DeepSeek, LLaMA) - Preferred
- Experience with FastAPI and async Python development - Preferred
- Familiarity with modern Python tooling (uv, mypy, ruff, bandit) - Preferred
- Experience with prompt management and versioning systems - Preferred
- Understanding of RAG architectures and token optimization - Preferred
- Experience writing and optimizing ML/AI tooling and components in C++ or Rust - Preferred
- Experience with LLMs and Infrastructure - Preferred
- Experience integrating with feature stores, feature caches and model serving platforms – Preferred
- Not Offered, now or in the future
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Ai Machine Learning Mlops