Senior MLOps Engineer, Flo

• 4+ years of industry experience in applied ML with recent experience in AI/LLM systems
• Strong proficiency with Python, and familiarity with Scala, Go, or Rust
• Cloud platform expertise with AWS, GCP, or Azure, including AI-specific services (SageMaker, Vertex AI, Azure AI)
• Databricks platform experience with Unity Catalog, MLflow, and Databricks Machine Learning for end-to-end AI/ML workflows
• Modern ML infrastructure experience with tools like MLflow or similar platforms
• Containerization & orchestration experience with Docker, Kubernetes, and ML-specific operators
• AI model serving experience with modern inference servers and API gateways for AI applications

Desirable

• In this role, you will own the end-to-end AI/ML pipeline, including CI/CD processes for ML and AI engineering workflows
• Your responsibilities will encompass managing code, versioning datasets, foundation and fine-tuned models, AI agents, and production endpoints
• This will enable ML Engineers to collaborate effectively, experiment efficiently, and scale rapidly across both traditional ML workloads and modern AI applications
• The AI Platform team acts as the central enabler of machine learning and AI initiatives across the organization. Its mission is to reduce operational overhead and maximize ROI from ML use cases
• As the backbone of ML operations, the team builds and maintains critical infrastructure, including feature stores, model deployment pipelines, experiment tracking systems, and monitoring frameworks
• By working closely with domain teams, the AI Platform team delivers scalable, high-quality solutions that accelerate time-to-market while ensuring compliance and maintaining the highest standards of performance
• Design and maintain automated pipelines for both traditional ML models and modern AI systems including LLMs, multimodal models, and AI agents
• Implement robust CI/CD practices for model training, fine-tuning, evaluation, and deployment across diverse AI workloads
• Orchestrate seamless deployment of models, AI agents, and inference endpoints with automated testing and rollback capabilities
• Build and maintain infrastructure for large language model inference, fine-tuning, and serving at scale
• Implement comprehensive monitoring for model performance, drift detection, AI safety metrics, and responsible AI compliance
• Evolve operational processes to support both traditional ML and cutting-edge AI technologies