Job Description
We are on the hunt for a visionary Senior AI/ML Engineer to join our elite engineering team at Skyline Dynamics. In this pivotal role, you will spearhead the development of next-generation artificial intelligence systems that power our global platforms. We are looking for a problem-solver who is passionate about pushing the boundaries of what is possible with machine learning and deep learning.
As a key member of our R&D division, you will work in a fast-paced, innovative environment where your code will directly impact millions of users. You will be responsible for designing scalable architectures, optimizing model performance, and bridging the gap between theoretical research and production-grade software.
Join us to build the future of intelligent automation and shape the landscape of technology for 2026 and beyond.
Responsibilities
- Lead Architecture: Design, implement, and deploy scalable machine learning models and deep learning pipelines for high-traffic production environments.
- Model Optimization: Continuously monitor, evaluate, and optimize model performance to ensure accuracy, speed, and cost-efficiency.
- Collaboration: Partner with cross-functional teams including data scientists, software engineers, and product managers to define technical requirements and deliver solutions.
- MLOps Implementation: Establish and maintain robust MLOps practices, including CI/CD pipelines, model versioning, and automated retraining workflows.
- Mentorship: Mentor junior engineers and conduct technical code reviews to maintain high standards of engineering excellence.
- Research Integration: Stay abreast of the latest advancements in AI research and integrate cutting-edge techniques into our product suite.
Qualifications
- Experience: Minimum of 5 years of professional experience in machine learning engineering or a related field.
- Technical Skills: Strong proficiency in Python, PyTorch, TensorFlow, or similar deep learning frameworks.
- Infrastructure: Deep understanding of cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Mathematics: Solid background in statistics, linear algebra, and calculus with the ability to apply these concepts to real-world problems.
- Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related technical discipline.
- Communication: Excellent verbal and written communication skills with the ability to explain complex technical concepts to non-technical stakeholders.