Principal Data Engineer - Machine Learning

About the Role

Key Responsibilities

• Design and lead the implementation of real-time data pipelines which transport billions of data points per day, with strong traffic variations around peak hours
• Design and deploy state-of-the-art ML (predominantly NLP and voice recognition) models that are optimised for both accuracy and throughput
• Transform prototypes into production-ready data and ML applications that meet throughput and latency requirements
• Deploy and manage data and ML infrastructure necessary for productionising code (Kafka, Docker, Terraform, etc)
• Build efficient data pipelines between on-premise and cloud environments to handle text and audio data processing loads for ML models
• Deploy NLP models in cloud environments (AWS SageMaker) through Jenkins
• Design and implement MLflow and other ML Ops applications to streamline ML workflows which adhere to strict data privacy and residency guidelines
• Communicate your work throughout the team and related departments
• Mentor and guide junior members of the team, establish and champion best practices and introduce fresh ideas and concepts

Requirements

• 10+ years of experience working on data processing and engineering for ML models, with 6+ years developing large-scale data and ML systems which receive billions of requests per day
• Expert level know-how of designing and implementing synchronous, asynchronous and batch data processing operations
• Expert level programming skills in Python, along with experience in using relevant tools and frameworks such as PyTorch, FastAPI and Huggingface; strong programming skills in Java are a plus
• Expert level know-how of ML Ops systems, data pipeline design and implementation, and working with ML platforms (preferably AWS SageMaker)
• Strong analytical and problem-solving abilities, with a keen eye for detail and accuracy
• Curiosity and a strong growth mindset with a demonstrable history of learning quickly in a loosely structured, rapidly changing environment
• Excellent collaboration and communication skills
• At least a bachelor's degree in computer science or other relevant fields

Nice to Have

• Strong programming skills in Java
• Experience with ML platforms, preferably AWS SageMaker

Qualifications

• At least a bachelor's degree in computer science or other relevant fields

Benefits & Perks

• Formal and on-the-job learning opportunities
• Comprehensive benefits package supporting a healthy lifestyle
• Hybrid working model with at least two days in the office per week
• Fostering a culture of collaboration, communication, performance and learning
• Opportunities to work in cross-functional teams and contribute to impactful projects