AI Engineer
- Design and develop machine learning models and algorithmic solutions that address complex, real-world challenges for users in Defence and National Security.
- Partner with product managers, product engineers, and domain experts to deliver ML-powered features from concept through to production.
- Engineer solutions with a deep understanding of algorithmic complexity and the operational cost of running models at scale.
- Communicate model choices, assumptions, and tradeoffs with clarity - whether you're speaking to fellow engineers, product stakeholders, or end users.
- Contribute to the development of robust, user-facing AI capabilities that are both high-performing and reliable - shipped fast, and iterated with care.
- Take full ownership of your work: from early experimentation through to deployment, monitoring, and ongoing optimisation.
- Help maintain the health and resilience of our production systems, debugging distributed pipelines when things go wrong.
- Stay close to the frontier of ML and AI research, continuously exploring ways to apply emerging techniques and tools to deliver better outcomes for users.
- Advanced degree (Master's or PhD) in Computer Science, Machine Learning, NLP, or a related technical field.
- 3+ years of hands-on experience applying machine learning in production settings, ideally within enterprise or mission-critical environments.
- Practical expertise with Generative AI techniques-fine-tuning and evaluating large language models (LLMs), building RAG pipelines, and experimenting with agentic AI workflows.
- Strong Python development skills and familiarity with modern ML and NLP frameworks and tooling (e.g. Hugging Face, spaCy, PyTorch, Scikit-learn).
- Familiarity with Kubernetes and infrastructure for deploying and scaling ML models is a plus.
- Exposure to systems integration challenges (e.g. connecting ML workflows with data stores like PostgreSQL or search systems like Elasticsearch) is valued.
- Clear, confident communicator who thrives in cross-functional settings and can bridge the gap between technical depth and product impact.
- A curious mindset - you stay sharp by experimenting with new tools and refining your approach based on what works.
- Phone Interview - Remote (30 mins)
- Technical Interview - Remote/In person (1 h)
- Final Interview - Onsite (1 h)
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