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Senior AI Engineer
We are looking for a Senior AI Engineer to join our growing Applied AI team. This is a hands-on, technically demanding role for someone who can contribute to building production AI systems while helping raise the technical bar of the team around them. You will work collaboratively across a fast-moving technology company, turning cutting-edge research into practical, scalable solutions. This role reports to the Applied AI Lead.
Responsibilities
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Data Handling and Modelling
- Lead data identification, cleaning, enrichment, preprocessing, feature engineering, and exploratory analysis to ensure fitness for AI workflows and to inform modelling and business decisions.
- Build, tune, and optimise machine learning, deep learning, and generative AI models, leveraging both established and cutting-edge techniques.
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LLM-Powered Systems
- Design and build LLM-powered systems — RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management, and secure model integration; selecting appropriately between standard and reasoning (test-time-compute) models, balancing capability against latency and cost.
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Agentic AI Systems
- Design and build agentic AI systems — tool-calling architectures, interoperability protocols (MCP, agent-to-agent), multi-agent orchestration, and multi-step reasoning with human-in-the-loop and appropriate trust, safety, and security boundaries.
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Tooling and Adaptation
- Leverage and contribute to agentic engineering tooling, including coding assistants, configurable permissions models, internal skills and plugins architectures, and sandboxed autonomous workflows integrated into CI/CD and delivery pipelines.
- Apply model adaptation techniques including parameter-efficient fine-tuning (LoRA, QLoRA), model distillation, and synthetic data generation for domain-specific and low-resource scenarios.
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Evaluation and Optimisation
- Develop evaluation frameworks covering performance, hallucination, safety, cost, and non-deterministic behaviour across classical and generative AI systems.
- Optimise model inference and end-to-end pipelines for speed, cost, memory footprint, and scalability, including for CPU-constrained and airgapped deployment targets.
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Production and Mentorship
- Develop and maintain production-grade model pipelines and supporting software with strong MLOps practices — covering real-time inference, batch processing, performance monitoring, drift detection, scheduled retraining, observability, governance, version control, and reproducibility.
- Provide technical guidance — architectural input, peer review, and mentoring of mid-level and junior engineers through code review, pairing, and knowledge sharing — raising the technical bar of the team and shaping secure, scalable, high-performing AI solutions.
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Collaboration and Research
- Work within Agile delivery teams — collaborating with data, platform, and software engineers to integrate AI into products, communicating clearly with technical and non-technical stakeholders, and championing engineering excellence and continuous improvement.
- Stay current with the latest research, frameworks, and trends across ML, deep learning, and GenAI, translating them into production-ready solutions.
Required Knowledge, experience, and values
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Experience and Education
- 5+ years of commercial experience in AI and machine learning engineering and development — academic research experience is highly valued alongside this, but a track record of commercial delivery is essential — with demonstrable delivery of production-grade models and systems across traditional ML, neural networks, and LLM-based systems.
- 5+ years of commercial experience working with Python; familiarity with C# is considered a bonus due to existing product integrations.
- Hold an advanced degree in machine learning, computer science, engineering, or a related discipline, with an MSc required and a PhD highly desirable.
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Technical Skills
- Deep hands-on experience with PyTorch, Scikit-learn, and the Hugging Face ecosystem, with familiarity with MLFlow and AzureML; capable of making architecture and implementation decisions.
- Strong competence with Git, pull requests, automated testing, CI/CD, MLOps, and ML pipelines; experience with Azure DevOps and cloud-based ML infrastructure (Azure, AWS, or GCP) is beneficial.
- Strong grounding in classical data science fundamentals — feature engineering, statistical analysis, experimental design, and model monitoring — alongside benchmarking and the evaluation of non-deterministic AI systems.
- Practical experience with inference optimisation including quantisation, latency reduction, cost-aware model selection, and resource-efficient deployment.
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Communication and Independence
- Communicate clearly, translating complex technical work into actionable recommendations, and produce high-quality documentation including technical decision records.
- Operate independently and make sound technical decisions within complex, ambiguous contexts, with a track record of owning problems end-to-end.
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Other
- Prior cybersecurity or business domain expertise is not a prerequisite, although would be highly relevant.
We encourage you to apply even if your experience is not a 100% match with the position.
Beneficial Knowledge, experience, and values
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Architecture and Scaling
- Familiarity with containerisation (Docker, Kubernetes), event-driven or microservices architectures, and distributed data processing at scale.
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RAG Systems and Evaluation
- Experience with embedding pipelines, vector search, and semantic retrieval for RAG systems.
- Knowledge of LLM evaluation techniques including structured evals frameworks, hallucination mitigation, benchmarking, and human-in-the-loop evaluation.
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AI Safety and Ethics
- Understanding of AI safety, responsible AI principles, governance, and regulatory and standards awareness (e.g. EU AI Act, NIST AI RMF, ISO/IEC 42001), including bias detection, explainability, and auditability.
- Familiarity with security best practices in AI systems, particularly around prompt injection risks, data handling, and model misuse.
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Applied AI in Cybersecurity
- Understanding of AI applied in cybersecurity contexts, including concept drift, adversarial robustness, and model assurance challenges.
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Data Engineering
- Exposure to data engineering practices including ETL/ELT pipelines, data versioning, and schema management.
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Multi-Modal Data
- Awareness of multi-modal data handling including vision, audio, or document-based inputs.
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- Community and Growth
- Contributions to open-source projects, research publications, or participation in AI/ML communities and conferences.
- Demonstrated curiosity and ability to balance research-oriented thinking with pragmatic engineering delivery in a commercial environment.
About Us
We didn’t start out as a traditional security product. In the beginning, Glasswall was one of only two file sanitization filters in the US Intelligence Community’s highly classified networks. We are rated #1 by the National Security Agency. We designed Glasswall CDR to protect businesses against the most advanced file-based threats. Today, we’re trusted by commercial and government organisations around the world.
In June 2025 Glasswall officially entered a new era of growth and innovation having been acquired by the leading private equity firm, PSG Equity. This marks a significant milestone for our company and one that underscores the strength of our business, the dedication of our team, and the exciting potential that lies ahead.
With PSG’s strong track record of scaling high-growth cybersecurity and technology businesses, we are better positioned than ever to accelerate innovation, expand into new markets, and deliver even greater value to our clients, employees, and stakeholders.
Cybersecurity is a mission-critical field, and we’ve always believed that staying ahead means moving faster, continually adapting to meet new challenges and investing more boldly in the future. This partnership empowers us to do exactly that while maintaining the same leadership, values, and commitment to excellence that have brought us this far.
We’re excited for what’s to come so now is a great time for you to join us on our journey.
Work/Life Balance
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to lifelong happiness and fulfilment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
Salary and Benefits
Glasswall offers a competitive salary and incentives package.
- We offer flexible and remote working options, with hybrid working from our office in the Central London area.
- Office travel and incidental WFH expense coverage.
- 25 days holiday (plus public holidays).
- Private Medical Insurance including mental health support and cancer care.
- Enhanced sick pay.
- Company sponsored life, critical illness, and income protection insurance.
- Contributory pension scheme.
- Access to ‘salary sacrifice’ benefits such as Cycle to Work and Tech Schemes.
A successful candidate will live in the United Kingdom and be comfortable working from home with some meetings being held in the London office.
We are looking for someone with relevant skills and experience, not a checklist that exactly matches the job description. We want to help you grow, and in return, you help us grow into a stronger, more inclusive organisation.
Glasswall is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected status.
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Jessica, London
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