Senior ML Engineer

Position Summary

Responsibilities

• **Deployment:**
• Deploy ML models and AI pipelines from PoC / development to production, ensuring they scale efficiently and maintain high performance through seamless CI/CD integration and orchestration.
• **Monitoring & Maintenance:**
• Implement monitoring and maintenance strategies for deployed models to ensure ongoing accuracy and reliability.
• **Model Optimisation & Pruning:**
• Optimise models for inference speed and resource efficiency using techniques such as quantisation, pruning, and knowledge distillation for edge and cloud deployment.
• **Data Preprocessing:**
• Perform data collection, cleaning, and feature engineering to prepare datasets for training.
• **Model Training & Tuning:**
• Implement continuous / semi-continuous training and evaluation workflows to maintain accuracy over time, and fine-tune models for optimal performance.
• **Collaboration:**
• Work with data scientists, software engineers, DevOps, and product managers to understand requirements and deliver ML solutions.
• **Documentation:**
• Maintain clear, organised documentation of code, models, and processes.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI, or a related field.
• 5 – 9 years of relevant experience.
• Proficiency in Python and libraries such as PyTorch, NumPy, Pandas, and Scikit-learn.
• Knowledge of model deployment, containerisation, and orchestration (Docker, Kubernetes).
• Knowledge of SQL and NoSQL databases.
• Familiarity with one or more cloud platforms (AWS, GCP, or Azure).
• Familiarity with MLOps tools such as MLflow, ClearML, Azure ML, or AWS SageMaker.
• Strong understanding of deep learning, reinforcement learning, and other ML techniques.

Preferred Qualifications

Core Technical Skills

Frameworks & Libraries

Serving & Deployment

Databases

Deep-Learning Architectures

Mlops & Distributed Computing

Message Brokers & Gpu

Cloud Platforms