Machine Learning Engineer

📋 Description

• MLOps framework: design, build and maintain the MLOps platform (experiment tracking, model
• ML productionizing: take models from research or prototype stage to robust, scalable production
• Automation of ML models: automate retraining, evaluation and deployment pipelines to reduce manual
• Streaming integration: integrate ML models with streaming data platforms (Kafka, Kinesis, Flink)
• Custom AI integration: design and integrate agentic workflows (LLM-based agents, tool calling

🎯 Requirements

• 5 to 8 years of experience in ML engineering, MLOps or backend infrastructure with ML systems in
• Strong software engineering fundamentals; comfortable owning services end to end
• Experience with model serving frameworks (Seldon, KServe, BentoML, TorchServe or similar) and
• Hands on experience with streaming systems (Kafka, Kinesis, Flink or similar)
• Familiarity with containerization and orchestration (Docker, Kubernetes)
• Experience with observability tooling (metrics, tracing, logging) for ML or distributed systems

🎁 Benefits

• Competitive Compensation
• Remote Work: you can work from everywhere
• Home Office Bonus: a one time allowance to set up your ideal home office
• Work Equipment
• Stock Options
• Health Plan wherever you are