In this role, you will be responsible for validating AI models developed by our Data Science team against real-world production systems, and then leading their optimization, deployment, and ongoing maintenance.
You will be part of the R&D team, working closely with engineers, data scientists, and product managers to ensure our AI solutions are scalable, reliable, and deliver long-term value.
If you enjoy working at the intersection of AI and engineering bringing models to life in production, optimizing for performance, and building reliable systems this role is for you!
Responsibilities
Lead the transition of AI models from proof-of-concept to full-scale production, ensuring they meet architectural, scalability, and performance standards.
Build observability and troubleshooting tools for AI services in production, including logging, performance tracking, and failure analysis pipelines.
Optimize inference performance, including latency, resource usage, and throughput, while maintaining model quality.
Manage model versioning and deployment readiness, including handoff processes, rollback plans, and configuration management.
Partner with the Data Science team to assess model readiness for production, validate input and output compatibility, and ensure assumptions align with real-world system behavior.
Collaborate cross-functionally with DevOps, backend engineers and data scientists to ensure scalable, secure, and cost-effective deployment of ML models
3-5 years of experience in ML Engineering, AI models deployment or MLOps roles.
Strong software engineering background with hands-on experience (Python or Java preferred) building and maintaining production ML services
Solid understanding of machine learning systems and inference pipelines
Familiarity with monitoring practices and production diagnostics for ML services (e.g logs, metrics, alerting)
Proven experience in optimizing AI models for performance (response-time, memory, CPU usage) particularly in real-time or large-scale environments.
Strong proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
Experience deploying AI solutions in cloud environments (AWS, GCP, or Azure)
