Senior Machine Learning Engineer, UnderwriteMe

• We are looking for candidates with a proven track record in the design and deployment of LLMs for applications within the NLP space, as well as established experience collaborating with product managers, developers, and fellow data scientists within a product-led SaaS environment
• Proven experience in applying AI techniques to solve real-world NLP problems, with a focus on delivering scalable, production-ready solutions
• Hands-on expertise in fine-tuning pre-trained models such as BERT, GPT, or similar transformer-based architectures for domain-specific tasks in the NLP space
• Experience in integrating Large Language Models (LLMs) into applications, with a focus on enabling structured responses, such as through APIs or with purpose-built LLMs
• Knowledge of prompt engineering techniques, including designing effective prompts for different tasks, optimizing input/output formats, and leveraging techniques such as few-shot learning
• Experience with OOP, and data-validation libraries such as Pydantic
• Deep familiarity with Python and its ecosystem for AI/ML, including libraries like PyTorch, Hugging Face Transformers, and scikit-learn
• Experience with data manipulation using libraries such as Pandas and NumPy, and familiarity with parallelization or asynchronous programming
• Proficiency in Test-Driven Development (TDD) and an understanding of Python testing libraries such as Pytest
• Cloud, CI/CD & MLOps Knowledge
• Experience taking models from experiments through to production deployments, with tools such as Docker, Kubernetes & serverless alternatives such as AWS Lambda
• Familiarity with MLOps tools such as MLFlow, Kubeflow or Sagemaker
• A strong knowledge of cloud platforms (ideally AWS) and their respective services for deploying robust, AI-heavy applications
• The ability to confidently communicate technical concepts to both a technical and non-technical audience (both verbally & written), for example when discussing results, technical approaches, or resolutions to potential blockers
• A deep appreciation for the possibilities of ML/AI on the application layer, and a strong desire to work on state-of-the-art applications where your ideas could directly translate to enormous business impact
• The ideal candidate will possess a product-led, entrepreneurial approach to their work, constantly evaluating new technologies which may facilitate improvements to the product and directly relate back to OKRs
• Experience in a fast-paced, start-up environment would be a bonus, with the ability to work both proactively and reactively
• Extensive experience of collaboration with engineers, architects & product teams to enable robust solutions to solve real, well-defined problems

Desirable

• We are currently seeking an innovative Senior Machine Learning Engineer to join UnderwriteMe within the Text Mining team. This role is pivotal in driving the creation of an innovative product set to disrupt the insurance market
• Working within a dynamic cross-functional team that operates based on OKRs (Objectives and Key Results), fostering collaboration among developers, QAs, data scientists, and data analysts, to consistently achieve tangible outcomes aligned with OKR targets
• Employing your deep understanding of AI and staying current with industry trends, you will play a pivotal role in shaping project execution by contributing to the OKR formulation process, and directly working towards those
• Crafting and refining Machine Learning models and algorithms to address complex product challenges
• Devising and implementing innovative data analysis and data mining strategies, extracting valuable insights from diverse data sources
• Harnessing the power of natural language processing (NLP) techniques to extract pertinent information from textual data
• Formulating predictive models to anticipate future trends, enabling informed decision-making
• Constructing automated ML workflows and integrating CI/CD practices to ensure seamless model deployment and recurrent refinement
• Architecting, deploying, and overseeing APIs for effective model delivery, while also leveraging external APIs to enhance functionality
• Establishing monitoring and logging systems to evaluate model performance, detect anomalies, and guarantee consistent model dependability and accessibility
• Collaborating closely with DevOps and IT teams to orchestrate the smooth transition of ML models into production environments, upholding scalability and security standards