Senior AI/ML Engineer (NLP & Knowledge Graphs)

We are looking for a Senior AI/ML Engineer to work on the shared AI layer behind our intelligence platform, with a focus on NLP and structured knowledge.

We operate on text, social media, imagery, geospatial, and time-series data at scale. Much of the AI challenge is turning that data into knowledge: recognising the real-world entities behind it, enriching them with context, and connecting them in knowledge graphs so analysts and AI systems can reason across sources, locations, and time.

You’ll bring hands-on experience applying ML and NLP models, including LLMs, to real-world data, modelling what they extract into knowledge graphs, and integrating your models into large-scale data pipelines. We care more about how you reason through model and architecture choices than about any specific framework.

Our knowledge graph direction is at an early stage, so you’ll have real ownership in shaping it, along with our models, pipelines, and technical direction. This is an engineering role, not a research one: you’ll own your solutions end-to-end, from experiments to production.

Requirements

• 5+ years of professional software engineering experience.
• Upper-intermediate English or higher.

Engineering Skills:

• Experience building and operating production-grade Python services that handle high-throughput workloads, including concurrent model and API calls, rate limits, retries, and failure handling.
• Experience working with distributed batch or streaming pipelines and integrating ML components into them using Celery, RabbitMQ, Apache Kafka, or similar technologies.
• Hands-on experience with FastAPI and Docker.

AI Skills:

• 3+ years of hands-on experience in ML/NLP engineering, including taking models from development to production.
• Production experience with information extraction from unstructured text, including entity and relation extraction, entity resolution, and entity linking.
• Hands-on experience designing and building knowledge graphs, including ontology / graph schema design and graph databases such as Neo4j. GraphRAG experience is relevant, but should be complemented by broader knowledge graph experience.
• Strong understanding of traditional NLP, transformer architectures, and language models, with the ability to choose the right approach based on the problem and production constraints.
• Hands-on experience with deep learning frameworks such as PyTorch.
• Experience evaluating ML/NLP systems, defining relevant quality metrics, and monitoring performance in production.

Nice to Have

• Hands-on experience with fine-tuning and/or self-hosted deployment of open models, including approaches such as LoRA/PEFT and SFT, model serving with vLLM or similar tools, and inference optimisation techniques such as quantisation.
• Experience with multimodal models, computer vision, or geospatial data, including satellite imagery and remote sensing.
• Background in threat intelligence, OSINT, geospatial intelligence, or defence.

We Offer:

• Opportunity to contribute to a mission-driven startup and support prestigious clients, including governments worldwide, enterprise clients, and leading NGOs in tackling global security challenges.
• Autonomy and freedom to drive experiments and bring your own ideas to life.
• Remote-first setup.
• Unlimited paid time off, plus company-wide holidays.
• Software and AI tools you need for your work are covered.