NLP Engineer

<!--block-->You will be joining the Parsing & Skills Intelligence team, which consists of a mix of Machine Learning/AI engineers and DevOps engineers. The team is responsible for designing, building, and deploying the AI models that power our products.<!--block-->Our AI models touch several subfields of Natural Language Processing (NLP): information extraction (e.g. parsing of CVs and job ads, skill extraction, job title normalisation), information retrieval (e.g. matching people and jobs), and knowledge mining. Our services process millions of complex documents daily in 28 languages with hundreds of thousands of matches.<!--block-->Technology stack: Python, FastAPI, PyTorch, Perl, Docker, Kubernetes, GitLab CI, Jenkins, Linux.<!--block-->Key responsibilities:

• <!--block-->Implementing solutions for building and deploying AI models in production, with a focus on fault tolerance, scalability, maintainability, and automation
• <!--block-->Leveraging modern LLMs to include/enhance parsing capabilities for CVs/job adverts for existing supported languages and new additional languages
• <!--block-->Collaborating with the team to design, implement, and optimise data processing pipelines and evaluation tools

• <!--block-->2+ years of experience working within a software development team as an engineer
• <!--block-->Experience with NLP
• <!--block-->Comfortable writing and debugging code in Python; understands the importance of readable code and eager to learn best practices
• <!--block-->Familiarity with frameworks like FastAPI, Flask, or Django, with experience developing and exposing/consuming APIs
• <!--block-->Understanding of containerisation and exposure to Docker
• <!--block-->Comfortable using Git for team collaboration
• <!--block-->Machine Learning fundamentals: foundational knowledge of ML concepts and practical experience (academic or personal projects) deploying a simple model using libraries like scikit-learn, PyTorch, or TensorFlow
• <!--block-->Experience manipulating and analysing data using libraries such as Pandas and NumPy
• <!--block-->A strong desire to learn NLP, MLOps principles, cloud infrastructure, and deployment practices
• <!--block-->BSc in Computer Science or a related technical field

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• <!--block-->GitLab pipelines exposure
• <!--block-->Kubernetes exposure

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• <!--block-->Fast-paced and dynamic environment where hard work and outstanding results are rewarded and celebrated
• <!--block-->Ample opportunities for career development with a strong focus on growth
• <!--block-->Prioritisation of promoting from within
• <!--block-->People-first culture where everyone's contributions are valued and respected
• <!--block-->Casual atmosphere combined with a fast-paced, agile environment
• <!--block-->Leaders who care deeply about employee development at all levels, emphasising emotional intelligence and accountability

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