Director, Machine Learning Engineering

We are seeking a highly skilled Machine Learning Engineering Director with a strong background in ML Ops, infrastructure, and software engineering. The ideal candidate will have at least 10+ years of total experience in the industry, including a minimum of 3 years in a leadership role. This position requires both leadership and hands-on technical expertise, managing a team of engineers while actively contributing to the design, development, and deployment of machine learning models and systems.

Key Responsibilities:

• Lead, mentor, and manage a team of machine learning engineers, providing guidance on best practices in ML Ops, infrastructure, and software engineering.
• Lead development of infrastructure for ML model training, testing, and deployment.
• Be hands-on in the design, development, and deployment of machine learning models and systems, ensuring they meet high standards of performance, scalability, and reliability.
• Collaborate with data scientists, product managers, and other stakeholders to define project requirements and deliverables.
• Develop and maintain ML Ops pipelines, ensuring efficient model training, deployment, and monitoring.
• Implement and manage infrastructure for large-scale data processing, model training, and inference.
• Drive continuous improvement in engineering practices, including code quality, testing, and deployment automation.
• Stay up-to-date with the latest trends and advancements in machine learning, software engineering, and cloud infrastructure to inform team strategy and direction.
• Manage project timelines, resources, and deliverables, ensuring projects are completed on time and within budget.
• Foster a culture of innovation, collaboration, and continuous learning within the engineering team.

Qualifications:

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field.
• 10+ years of experience in software engineering, with a focus on machine learning, ML Ops, and infrastructure.
• Minimum of 3 years of experience in a leadership or management role, with a proven track record of leading engineering teams to successful project outcomes.
• Strong understanding of machine learning frameworks, tools, and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with ML Ops practices, including model versioning, continuous integration, and automated deployment.
• Proficiency in software engineering practices, including object-oriented design, code versioning, and testing.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and distributed computing.
• Strong problem-solving skills, with the ability to lead teams in troubleshooting complex technical challenges.
• Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.
• Demonstrated ability to manage multiple projects simultaneously, prioritizing tasks and managing resources effectively.

Preferred Qualifications:

• Experience with containerization technologies (e.g., Docker, Kubernetes).
• Knowledge of big data technologies (e.g., Hadoop, Spark) and data engineering practices.
• Experience with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI).

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