Design, build, and maintain model inference and serving systems, including integration with AI gateways.
Own and evolve fine-tuning pipelines (e.g., LoRA / PEFT) using internal model tooling.
Develop and maintain model evaluation, regression detection, and rollout workflows.
Collaborate with AI researchers to transition research models into production-ready assets.
Optimize inference systems for latency, throughput, stability, and cost efficiency.
Implement best practices for model versioning, deployment, rollback, and monitoring.
Partner with agent and platform engineers to ensure smooth integration between model systems and agent runtimes.
Provide technical leadership and mentorship on ML system design and operational excellence.

Bachelor’s degree in Computer Science, Software Engineering, or a related field.
5+ years of experience in software engineering, with significant ownership of backend or distributed systems.
Strong proficiency in Python, with experience building production services.
Hands-on experience with AI/ML model serving, inference pipelines, or ML systems engineering.
Experience designing reliable, scalable systems for production environments.
Familiarity with cloud platforms (AWS, GCP) and containerized environments (Docker, Kubernetes).
Strong debugging skills across system, data, and model-facing failures.
Excellent communication skills and ability to collaborate across research and engineering teams.

AI Engineer, ML Systems

Key skills