Job Description:   

We are looking for a seasoned Principal Machine Learning Engineer to lead the design, development, and deployment of scalable, production-grade machine learning systems. As a key technical leader in our AI & Machine Learning department, you will drive the architecture and implementation of advanced ML models that solve critical business problems, mentor engineering teams, and collaborate with cross-functional stakeholders to ensure high-quality AI product delivery.   

Key Responsibilities:   

• Architect, develop, and optimize large-scale machine learning models and systems for real-world applications.   
• Lead technical strategy and decision-making for ML infrastructure, tools, and workflows.   
• Collaborate with data scientists, software engineers, product managers, and business teams to translate requirements into scalable ML solutions.   
• Oversee the entire ML lifecycle including data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.   
• Implement best practices for model versioning, reproducibility, testing, and continuous integration/continuous deployment (CI/CD) pipelines.   
• Drive innovation by researching and integrating state-of-the-art ML algorithms and techniques.   
• Mentor and guide junior and mid-level ML engineers to enhance team capabilities and technical excellence.   
• Ensure ML solutions comply with security, privacy, and ethical AI standards.   
• Provide technical leadership and support in AI projects, helping to define roadmaps and deliverables.   

Requirements:

• Extensive experience (typically 7+ years) in machine learning engineering with a strong track record of delivering production ML systems.   
• Deep expertise in ML frameworks such as TensorFlow, PyTorch, or similar.   
• Proficiency in programming languages such as Python, Scala, or Java.   
• Strong background in software engineering principles, data structures, algorithms, and system design.   
• Experience with cloud platforms (AWS, Azure, GCP) and ML infrastructure tools (Kubeflow, MLflow, Docker, Kubernetes).   
• Solid understanding of data engineering, distributed computing, and scalable architecture design.   
• Excellent problem-solving skills and ability to lead technical discussions and design reviews.   
• Advanced degree (Master’s or PhD) in Computer Science, Machine Learning, Artificial Intelligence, or related field is preferred.   
• Strong communication skills with experience in mentoring and leadership roles.