Job Description
We are on a mission to revolutionize the future of data intelligence. Zenith Data Systems is seeking a visionary Senior AI Engineer to lead the development of our next-generation machine learning infrastructure. If you are passionate about pushing the boundaries of what's possible with Large Language Models (LLMs) and scalable AI architecture, we want to hear from you.
In this role, you will not just write code; you will architect solutions that power the decisions of Fortune 500 clients. You will work in a collaborative, high-performance environment that values innovation, speed, and technical excellence.
What You Will Do:
- Architect & Deploy: Design, train, and deploy cutting-edge machine learning models and AI agents to production environments.
- Optimize Performance: Implement MLOps best practices to ensure scalability, reliability, and low-latency inference.
- Collaborate: Partner with data scientists and product managers to translate complex business requirements into robust technical solutions.
- Innovate: Experiment with novel architectures and algorithms to stay ahead of industry trends in AI and Deep Learning.
Why Apply?
Join a team where your work has a direct impact on the global market. We offer a competitive compensation package, equity opportunities, and a culture that fosters continuous learning and growth.
Responsibilities
- Develop and maintain scalable machine learning pipelines using Python and modern frameworks.
- Optimize data preprocessing and feature engineering for maximum model accuracy.
- Collaborate with cross-functional teams to integrate AI features into web and mobile applications.
- Ensure model security, compliance with data privacy regulations (GDPR/CCPA), and ethical AI standards.
- Conduct A/B testing and performance monitoring to continuously improve model outputs.
- Document technical architecture and contribute to the engineering knowledge base.
Qualifications
- Bachelor’s degree in Computer Science, Mathematics, or a related field (Master’s preferred).
- 5+ years of professional experience in Machine Learning Engineering or Data Engineering.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Deep understanding of MLOps, CI/CD pipelines, and data orchestration tools (Airflow, Kubeflow).
- Experience with vector databases and RAG (Retrieval-Augmented Generation) architectures.