MLOps Engineer

Responsibilities

• Build, deploy, and maintain production AI systems.
• Take ownership of end-to-end ML pipelines (data ingestion, training, validation, deployment, monitoring).
• Optimize models for performance, scalability, and reliability.
• Work closely with founders, engineering, and product teams to ship AI features.
• Monitor live models, handle retraining, and improve model quality over time.
• Improve our MLOps and deployment infrastructure.

Requirements

• 3+ years of experience building and running production AI / ML systems.
• Strong computer science background and understanding of natural language processing/computer vision.
• Solid hands-on experience with Python, PyTorch, SQL, graph databases, load balancing, etc.
• Experience tracking experiments and models using, e. g., MLflow.
• Strong understanding of AI foundations, LLMs, and model internals.
• Practical experience deploying AI systems using Docker, Docker Compose, and Kubernetes.
• Familiarity with model monitoring, versioning, and CI/CD for ML.
• Deep understanding and practical experience of basic (Gen)AI approaches, e. g., RAG.
• Practical experience with a multi-agent AI system is a plus.
• 3+ years of work experience with machine learning.
• 3+ years of work experience with Python (programming language).
• 1+ years of work experience with AI agents.
• Experience scaling ML systems in production.
• Strong MLOps mindset.
• Cloud infrastructure experience (AWS, GCP, Azure).
• Experience in a startup or product-driven environment.
• Experience owning AI features in end-to-end production grade.
• Experience with MCP / A2A protocol and agentic frameworks is a plus.
• DevOps experience is a plus.
• Experience with the ETL process is a plus.