What youll be doing:
Architecting, developing and optimizing scalable infrastructure for deploying security and networking AI models and agents in production.
Managing ML/agentic workflows to ensure performance, high availability, resource efficiency, and cost-effectiveness.
Designing and implementing pipelines and frameworks for AI training, inference, and experimentation.
Partnering with data scientists and security architects to operationalize AI agents, including packaging and integration with existing systems. This includes contributing to and reviewing code, design documents, and test plans.
Partnering with DevOps teams to integrate pipelines and workflows into CI/CD processes, ensuring reliable deployments and rollbacks.
Building proactive monitoring systems to identify issues in quality and infrastructure before they impact production.
Implementing access controls, authentication mechanisms, and encryption standards to keep our AI models and data secure.
Documenting guidelines and leading knowledge-sharing sessions to elevate the teams collective development expertise.
What we need to see:
BSc/MSc in CS/CE or related field (or equivalent experience).
At least 8 years of experience in ML engineering with a track record of deploying LLMs and agents to production at scale (including distributed environments).
Proficiency in Python and/or C++, with a deep understanding of ML/AI frameworks.
Hands-on experience with microservices, container orchestration, and cloud platforms for large-scale training and inference workloads.
Knowledge of ML training and inference optimization techniques.
Understanding of build infrastructure and CI/CD tools and practices (e.g. GitLab, GitHub Actions, Jenkins)
Experience with teaching and mentoring.
You are a proactive owner who takes pride in your work but remains humble and approachable. You believe that "how" we build is just as important as "what" we build.
Excellent collaboration skills, with the ability to explain complex infra concepts to non-technical stakeholders clearly and kindly.
Ways to stand out from the crowd:
Experience deploying and optimizing generative models and multi-agent systems for performance.
Deep systems knowledge (Linux internals, network protocols, or high-performance computing).
A background in security research, including knowledge of firewalls, intrusion detection, or network architectures.
