Job Description
Join the Future of Intelligence
Nexus Core Systems is pioneering the next generation of artificial intelligence solutions. We are seeking a visionary Senior AI Engineer to lead the development of scalable machine learning models that power our flagship products. If you are passionate about pushing the boundaries of what's possible in neural networks and deep learning, we want to hear from you.
What You Will Do
As a Senior AI Engineer at Nexus, you will bridge the gap between theoretical research and production-grade software. You will work closely with a cross-functional team of data scientists, engineers, and product managers to deliver cutting-edge AI capabilities.
Responsibilities
- Design and Implement: Develop, train, and deploy state-of-the-art machine learning models and deep learning architectures.
- Optimization: Engineer efficient data pipelines and model inference systems to ensure low latency and high scalability.
- Research & Development: Stay ahead of the curve by exploring emerging AI trends, conducting literature reviews, and implementing novel algorithms.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Collaboration: Work closely with product teams to define AI requirements and translate business needs into technical solutions.
- Production Support: Monitor model performance in production environments, troubleshoot issues, and implement A/B testing strategies.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in AI/ML engineering, with a strong focus on Deep Learning.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Experience with distributed computing frameworks (Spark, Kubeflow).
- Languages: Strong command of English and excellent communication skills.
- Problem Solving: Demonstrated ability to solve complex, unstructured problems in dynamic environments.
- Tools: Familiarity with MLOps tools (MLflow, Docker, Kubernetes) and version control systems (Git).