Job Description
We are on the precipice of a technological revolution, and we need a visionary Senior AI/ML Engineer to help us architect the future. By 2026, we aim to redefine the boundaries of artificial intelligence and machine learning applications across global industries. This is not just a job; it is an opportunity to shape the next decade of technology.
As a key member of our Future Systems division, you will work in a collaborative, high-performance environment focused on cutting-edge research and scalable deployment. You will have the autonomy to experiment with the latest models while ensuring robust, production-grade reliability. If you are passionate about the trajectory of AI and want to build systems that will define the landscape of 2026 and beyond, we want to hear from you.
Responsibilities
- Lead Research & Development: Spearhead the design and implementation of advanced neural networks and machine learning models focused on generative AI and predictive analytics.
- Model Deployment: Oversee the end-to-end lifecycle of ML models, from training and validation in sandbox environments to scalable deployment in production infrastructure.
- System Architecture: Design scalable data pipelines and architecture that can handle petabyte-scale data processing with low latency.
- Innovation Strategy: Stay ahead of the curve on emerging AI trends, evaluating new technologies (e.g., LLMs, reinforcement learning) to integrate into our product roadmap.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and engineering teams to translate complex technical requirements into actionable software solutions.
- Mentorship: Guide junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
Qualifications
- Experience: 7+ years of professional experience in software engineering, machine learning, or a related field, with a strong portfolio of published research or deployed projects.
- Technical Stack: Proficiency in Python, C++, and experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX.
- Education: Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related field is highly preferred.
- Algorithms: Deep understanding of classical machine learning algorithms and modern deep learning architectures (CNNs, RNNs, Transformers).
- Cloud Expertise: Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Problem Solving: Exceptional ability to solve complex, unstructured problems with a focus on scalability and efficiency.