Job Description
We are looking for a visionary Senior AI & Machine Learning Engineer to join FutureScale AI. Our mission is to architect the intelligent systems that will define the technological landscape by 2026 and beyond. You will be at the forefront of innovation, building scalable, robust, and ethical AI solutions that solve complex real-world problems.
In this role, you will lead a team of data scientists and engineers, pushing the boundaries of what is possible with Deep Learning, Natural Language Processing, and Predictive Analytics. If you are passionate about the future of technology and want to make a tangible impact, we want to hear from you.
Responsibilities
- Lead Model Development: Design, train, and deploy state-of-the-art machine learning models using Python, TensorFlow, and PyTorch.
- System Architecture: Architect scalable data pipelines and cloud infrastructure (AWS/Azure) to support high-volume inference.
- Research & Innovation: Stay ahead of industry trends, including Generative AI and Autonomous Systems, to integrate cutting-edge advancements into our product suite.
- Team Leadership: Mentor junior engineers and data scientists, conducting code reviews and fostering a culture of technical excellence.
- Performance Optimization: Continuously monitor model performance and optimize latency and throughput for real-time applications.
- Cross-Functional Collaboration: Work closely with product managers and stakeholders to translate business requirements into technical specifications.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Statistics, Mathematics, or a related field (or equivalent practical experience).
- Experience: 5+ years of professional experience in machine learning engineering, with at least 2 years in a leadership or senior technical role.
- Technical Skills: Strong proficiency in Python, SQL, and experience with deep learning frameworks (TensorFlow/PyTorch/Keras).
- Cloud Mastery: Proven experience deploying models on cloud platforms (AWS, GCP, or Azure) using containerization (Docker/Kubernetes).
- Problem Solving: Deep understanding of MLOps, data preprocessing, feature engineering, and model evaluation metrics.
- Communication: Excellent verbal and written communication skills with the ability to explain complex technical concepts to non-technical stakeholders.