Job description
NovaAI Labs is seeking a Senior AI Engineer to accelerate our next generation of intelligent products. You will design, implement, and deploy scalable AI systems that power real-world applications in healthcare, finance, and robotics.
In this role, you will own end-to-end AI workstreams—from problem framing and data discovery to model deployment and monitoring in production. You will collaborate with Product, Data Engineering, and Software teams to deliver high-impact solutions that move the business forward.
We’re looking for a collaborative problem-solver who thrives in a fast-paced, data-driven environment and who is passionate about building robust, responsible AI.
Responsibility
- Lead design and implementation of production-ready ML models that solve high-value business problems.
- Architect scalable data pipelines and feature stores with emphasis on quality, latency, and governance.
- Build, test, and monitor ML systems in production; implement MLOps practices for CI/CD and reliability.
- Collaborate with Product and Engineering teams to translate business needs into ML solutions.
- Mentor junior engineers and promote best practices in model interpretability and fairness.
- Experiment with new algorithms and datasets to continuously improve model performance.
- Define metrics, dashboards, and monitoring strategies to measure impact and ensure responsible AI.
Qualification
- Bachelor’s or Master’s in computer science, mathematics, statistics, or a related field; PhD a plus.
- 5+ years of hands-on experience building production ML systems, preferably in an AI product environment.
- Strong programming skills in Python; experience with TensorFlow or PyTorch; familiarity with JAX is a plus.
- Deep understanding of ML algorithms, feature engineering, evaluation, and deployment strategies.
- Experience with cloud platforms (AWS, GCP, or Azure) and MLOps tooling for monitoring and governance.
- Excellent communication, collaboration, and problem-solving abilities; demonstrated ability to translate business needs into technical solutions.
- Familiarity with data governance, privacy, bias mitigation, and model interpretability; commitment to responsible AI.
- Open-source contributions or publications are a plus; hands-on experience in NLP or computer vision is beneficial.