SmartRecruiting is looking on behalf of its client for a hardworking and creative Machine Learning Interface Engineer.
DataTech company operating in the financial and professional services sector. We are creating a privacy preserving, sector agnostic, plug and play, data access and data collaboration platform at a scale that makes it critical infrastructure.
- As a developer in Machine Learning (ML) interfaces, you will apply your expertise of designing and building distributed systems to develop ML pipelines all through training and deployment into production.
- Understanding the requirements of the ML research team, articulating them to the rest of the technical team.
- Building and deploying a Federated Learning framework built on principles of modularity, scalability & maintainability.
- Owning, designing, developing, and testing all necessary API’s predominantly in Python (with the option to leverage other languages including Java and Go) to RegulAition’s standards, quality and architecture.
- Advanced Computer Science knowledge, e.g. development and code review best practices, software engineering, algorithms, data structures, object-oriented design, design patterns and microservices.
- Advanced knowledge in parallel and distributed computing – messaging, microservices-based architectures, Big Data architectures such as Hadoop/Spark, containerisation, orchestration etc.
- Proficiency in Java or another non-scripted language.
- Demonstrable knowledge of Python and ability to drive the ML ecosystem/architecture.
- Familiarity with Machine Learning concepts and interacting with models produced using industry-standard Python libraries.
- Proficiency in service construction and development of RESTful APIs in Python, knowledge of Swagger or Postman.
- Working knowledge of integrating with cloud hosting providers such as AWS or GCP.
- Knowledge of DevOps tools such as Kubernetes (and Ingress), message brokers (e.g. RabbitMQ) and CI/CD, including GitLab CI or Jenkins.
- Knowledge of NoSQL databases (e.g. MongoDB or PostgreSQL).
- Good communication skills in order to collaborate on productionising
- ML/NLP projects with the other data scientists and/or interact with RegulAItion’s clients in order to pitch/present on your ML implementation, including articulating in UML.
Nice to have:
- Master’s Level qualification in a CS/Engineering or a numerically intensive field.
- Good theoretical grounding and practical experience in core machine learning concepts, techniques and frameworks, Deep Learning frameworks (e.g. Tensorflow, PyTorch or Keras) and traditional Python ML libraries (e.g. Scikit-learn, Pandas, NumPy, Keras, Gensim, NLTK or spaCy)
- AWS certification
- Knowledge of Federated Learning
- Knowledge of ELK / ELG
- Knowledge of distributed ledger technologies e.g. Hyperledger Fabric
- Understanding of authentication and authorization protocols, e.g. OAuth 2.0
- Knowledge of Ray Core and Ray Tune
- You will operate on a “remote work first” basis, offering competitive packages.