Lead Machine Learning Engineer

Placements24; Kempton Park; Remote; ZAR 1,200,000 - 1,900,000; Full time

Benefits offered by this job

Equity options

Health coverage

Remote stipend

Home office setup

Unlimited PTO

Professional development

Job summary

Placements24 is seeking a visionary Lead Machine Learning Engineer to spearhead remote-first AI initiatives. You will design, develop, and deploy sophisticated ML solutions, leading a talented team of engineers and architecting robust pipelines for scalable, production-ready AI products.

You will mentor engineers, optimize models for performance, and define ML roadmaps in collaboration with data scientists, software engineers, and product managers.

Qualifications

• Master's or PhD in Computer Science, Engineering, or related field.
• 7+ years of experience shipping ML products as a ML/AI Engineer.
• Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
• Strong experience with MLOps tools (Kubeflow, MLflow, Docker, Kubernetes).
• Experience with distributed systems and cloud platforms (AWS, Azure, GCP).
• Excellent leadership, communication, and problem-solving skills for remote environments.

Responsibilities

• Lead design, development, and deployment of end-to-end ML systems.
• Architect scalable, production-ready ML pipelines for data processing and model deployment.
• Collaborate with data scientists, engineers, and product managers to define ML strategies.
• Mentor and guide a team of ML engineers toward technical excellence.
• Drive best practices in MLOps, including monitoring, versioning, and CI/CD for ML workflows.

Skills

Leadership

Remote collaboration

Communication

Mentoring

Strategic planning

Problem solving

Education

Master's or PhD in CS/Engineering

Tools

TensorFlow

PyTorch

scikit-learn

Kubeflow

MLflow

Docker

Kubernetes

AWS

Azure

GCP

Job description

About the Role

Our client is actively seeking a visionary Lead Machine Learning Engineer to spearhead their remote-first AI initiatives. This role is crucial for designing, developing, and deploying sophisticated machine learning solutions that drive innovation and business value. You will lead a talented team of engineers, architecting robust ML pipelines and ensuring the scalability and reliability of our AI-powered products. This is an exceptional opportunity for an experienced ML professional who thrives in a fully remote setting and is passionate about building and mentoring high-performing teams while tackling complex, impactful challenges in the AI and Emerging Technologies space.

Key Responsibilities

• Lead the design, development, and implementation of end-to-end machine learning systems.
• Architect and build scalable, production-ready ML pipelines for data processing, model training, and deployment.
• Collaborate closely with data scientists, software engineers, and product managers to define ML strategies and roadmaps.
• Mentor and guide a team of machine learning engineers, fostering a culture of technical excellence and continuous improvement.
• Optimize ML models for performance, efficiency, and scalability across distributed systems.
• Ensure best practices in MLOps, including monitoring, versioning, and CI/CD for ML workflows.

Requirements

• Master's or PhD degree in Computer Science, Engineering, or a related field.
• Extensive experience (7+ years) in machine learning engineering, with a proven track record of shipping ML products.
• Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong experience with MLOps tools and practices (e.g., Kubeflow, MLflow, Docker, Kubernetes).
• Experience in designing and managing distributed systems and cloud platforms (AWS, Azure, GCP).
• Excellent leadership, communication, and problem-solving skills, suited for a remote environment.

Benefits

• Highly competitive salary with equity options.
• Comprehensive health, dental, and vision coverage.
• Generous remote work stipend and home office setup support.
• Unlimited paid time off and flexible work schedules.
• Significant investment in professional development and learning opportunities.