About the position
We are looking for a highly skilled Applied AI Engineer with a strong engineering mindset to bridge the gap between research and production.
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!
AI System Design & Productionization
Lead the transition of AI models from proof-of-concept to full-scale production, ensuring they
meet architectural, scalability, and performance standards.
Ensure models developed in research align with our product requirements, system architecture,
and real-world constraints.
Collaborate with DevOps and cloud infrastructure teams to deploy AI solutions in robust,
scalable environments.
3-5 years of experience in ML Engineering, AI models deployment or MLOps roles.
Hands-on experience optimizing AI models for real-time or large-scale applications.
Strong proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
Production-level coding skills in Python, Java, or other languages used in AI/ML deployment.
Experience deploying AI solutions in cloud environments (AWS, GCP, or Azure) and
working with containerized infrastructure (Kubernetes, Docker).
Excellent analytical and problem-solving abilities, with a focus on model optimization, performance tuning, and long-term reliability.
Nice to have
Experience working with vector databases and generative AI APIs (e.g., OpenAI,
Anthropic).
Familiarity with MLOps practices, including automated model training pipelines, CI/CD for
AI, and real-time inference systems.
Understanding of AI governance, compliance, security, and responsible AI principles.
