Principal Data Engineer – Machine Learning
The driving force behind our Machine Learning and Data Science infrastructure at Mimecast
Embrace the incredible opportunities that lie within Mimecast, where innovation and impact converge. The cybersecurity industry is experiencing exponential growth, and by joining us, you'll be at the forefront of this ever-evolving landscape. The field is rapidly changing, as threat actors employ AI to scale up phishing and social engineering operations.
Why Join Our Team?
“You'll have the chance to build large-scale data pipelines moving billions of data points daily in real-time, and develop, deploy and utilise cutting-edge ML models, empowering you to thwart those cyber villains and safeguard businesses and individuals alike. As a company that is well-established and committed to growth, we are actively expanding our ML team with a Principal Data Engineer – Machine Learning role which is amongst the most senior roles in the team, directly reporting to the Director of Data Science. Join us on this exhilarating journey, where you'll shape the future of cybersecurity by developing large-scale data products for ML models that push the boundaries of innovation and make an indelible impact in protecting our digital world.”– Hiring Manager
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
Experience
- 10+ years of experience working on data processing and engineering for ML models, with 6+ years developing large-scale data and ML systems twhich receive billions of requests per day
- Expert level know-how of designing and implemention 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
What We Bring
Join our Machine Learning and Data Science team to accelerate your career journey, working with cutting-edge technologies and contributing to projects that have real customer impact. You will be immersed in a dynamic environment that recognizes and celebrates your achievements.
Mimecast offers formal and on the job learning opportunities, maintains a comprehensive benefits package that helps our employees and their family members to sustain a healthy lifestyle, and importantly - working in cross functional teams to build your knowledge!
Our Hybrid Model:We provide you with the flexibility to live balanced, healthy lives through our hybrid working model that champions both collaborative teamwork and individual flexibility. Employees are expected to come to the office at least two days per week, because working together in person:
- Fosters a culture of collaboration, communication, performance and learning
- Drives innovation and creativity within and between teams
- Introduces employees to priorities outside of their immediate realm
- Ensures important interpersonal relationships and connections with one another and our community!
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