Job Description
Join the Future of Intelligence
Horizon AI Labs is pioneering the next generation of artificial intelligence solutions for enterprise-scale challenges. We are looking for a visionary Senior AI Engineer to lead the development of our proprietary machine learning models. You will be at the forefront of innovation, transforming complex data into actionable insights that drive business growth.
As a key member of our technical team, you will collaborate with world-class researchers and engineers to deploy state-of-the-art deep learning architectures. We offer a competitive salary, equity packages, and a flexible remote-first culture designed for high performance.
Why Join Us?
- Work with cutting-edge technologies like LLMs and Computer Vision.
- Competitive compensation and comprehensive benefits package.
- Opportunity to mentor junior talent and shape engineering standards.
Responsibilities
- Model Development: Design, train, and deploy robust machine learning and deep learning models using Python and frameworks like PyTorch and TensorFlow.
- System Optimization: Optimize existing models for inference speed, scalability, and accuracy to reduce latency in production environments.
- Data Engineering: Collaborate with data scientists to clean, preprocess, and structure large datasets to ensure high-quality model training.
- MLOps Implementation: Establish and maintain MLOps pipelines using tools such as Kubernetes, Docker, and MLflow to streamline the model lifecycle.
- Research & Innovation: Stay abreast of the latest academic research in AI and implement novel techniques to improve product performance.
- Technical Leadership: Provide technical guidance to the engineering team, conduct code reviews, and contribute to architectural decisions.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related technical field.
- Experience: 5+ years of professional experience in AI/ML engineering, with a proven track record of deploying production-ready models.
- Programming: Expert-level proficiency in Python, including experience with scientific computing libraries (NumPy, Pandas).
- Frameworks: Strong experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- MLOps: Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Communication: Excellent verbal and written communication skills, with the ability to translate complex technical concepts for non-technical stakeholders.