Machine Learning Engineer

The Senior AI/ML Platform Engineer is responsible for leading the design, implementation, and optimization of scalable machine learning infrastructure. This role ensures that AI/ML models are efficiently deployed, managed, and monitored in production environments while providing mentorship and technical leadership to junior engineers.

To apply, please follow the link to our career page below.

https://jobs.lever.co/welocalize/ad578606-1e6e-4928-a146-cc05759249d9

Key Responsibilities:

• Architectural Leadership: Lead the design and development of scalable, secure, and efficient AI/ML platform architecture, ensuring robust and reliable infrastructure.
• Automation & Deployment: Develop and implement advanced automation pipelines for model deployment, monitoring, and rollback, enhancing operational efficiency.
• Cross-Functional Collaboration: Collaborate with cross-functional teams, including data scientists and product managers, to define platform requirements and support seamless model integration.
• Performance Optimization: Drive performance tuning, load balancing, and cost optimization strategies to ensure the platform's efficiency and scalability.
• Mentorship & Leadership: Mentor junior platform engineers, providing technical guidance and fostering a culture of best practices and continuous learning.
• Incident Management: Conduct post-mortems and root cause analysis for system failures and performance issues, implementing corrective actions to prevent recurrence.

Education:

• Educational Background: Bachelor's or Masters degree in Computer Science, Engineering, or a related field.

Experience:

• 5+ years of experience in AI/ML platform or infrastructure engineering, with a proven track record in leading and executing complex projects.

Technical Expertise:

• Expertise in cloud-based solutions (e.g., AWS, GCP, Azure), distributed systems, and microservices architecture.
• Proficiency in Terraform, Docker, and advanced automation tools.
• Strong understanding of machine learning frameworks (e.g., TensorFlow, PyTorch) and MLOps practices.

Problem-Solving Skills: Excellent problem-solving skills with a proactive approach to identifying and addressing technical challenges.

Leadership Skills: Strong leadership and mentoring skills, with the ability to guide and inspire engineering teams.

Communication Skills: Excellent communication skills, with the ability to articulate technical concepts to both technical and non-technical stakeholders.