Glasswall, LLC
We are looking for a Senior AI Engineer to join our growing Applied AI team. This is ahands-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, turningcutting-edgeresearch into practical, scalable solutions.This role reportsto the AppliedAILead. Responsibilities Lead data identification, cleaning, enrichment, preprocessing,feature engineering,andexploratoryanalysis to ensure fitness for AI workflows and to inform modelling and business decisions. Build, tune, andoptimisemachine learning, deep learning, and generative AI models,leveragingboth established andcutting-edgetechniques. Designand buildLLM-powered systems- RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management,and secure model integration;selectingappropriately between standard and reasoning (test-time-compute) models, balancing capability against latency and cost. Designand buildagentic AI systems- tool-calling architectures, interoperability protocols (MCP, agent-to-agent), multi-agent orchestration, and multi-step reasoning with human-in-the-loop andappropriate trust, safety, and security boundaries. Leverageand contributeto agentic engineering tooling, including coding assistants,configurable permissions models,internal skills and plugins architectures, and sandboxed autonomous workflows integrated intoCI/CDand delivery pipelines. Applymodel adaptation techniques including parameter-efficient fine-tuning(LoRA,QLoRA), modeldistillation, and synthetic data generation for domain-specific and low-resource scenarios. Develop evaluation frameworks covering performance, hallucination, safety, cost, and non-deterministicbehaviouracross classical and generative AI systems. Optimisemodel inference and end-to-end pipelines for speed, cost, memory footprint, and scalability, including for CPU-constrained andairgappeddeployment targets. Develop andmaintainproduction-grade model pipelines and supporting software with strongMLOpspractices - covering real-time inference, batch processing, performance monitoring, drift detection, scheduled retraining, observability, governance, version control, and reproducibility. Providetechnicalguidance- 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. 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 5+ years of commercialexperience in AI and machine learning engineering and development - academic research experience is highly valued alongside this, buta track recordof commercial delivery is essential -withdemonstrabledelivery of production-grade models and systems across traditional ML, neural networks, and LLM-based systems. 5+years of commercial experience working withPython;familiaritywith 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 MScrequiredand a PhD highly desirable. Deep hands-on experience withPyTorch, Scikit-learn, and the Hugging Face ecosystem, with familiarity withMLFlowandAzureML; 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 inclassical data science fundamentals - feature engineering, statistical analysis, experimental design, and model monitoring - alongside benchmarkingand the evaluation of non-deterministic AI systems. Practical experience with inferenceoptimisationincludingquantisation, latency reduction, cost-aware model selection, and resource-efficient deployment. 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, witha track recordof owning problems end-to-end. Prior cybersecurity or business domainexpertiseis 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 Familiarity with containerisation (Docker, Kubernetes), event-driven or microservices architectures, and distributed data processing at scale. 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. Understanding of AI safety, responsible AI principles, governance, and regulatoryand standardsawareness (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. Understanding of AI applied in cybersecurity contexts, including concept drift, adversarial robustness, andmodel assurance challenges. Exposure to data engineering practices including ETL/ELT pipelines, data versioning, and schema management. Awareness of multi-modal data handling include vision, audio, or document-based inputs. 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 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. Inclusion At Glasswall we believe that diversity of people and thought are central to our purpose. We are committed to making Glasswall a company that is attractive to people of many different backgrounds. This includes diversity in every sense of the word: those with different backgrounds, ages, ethnicities, gender identities, sexual orientations, ways of thinking and those with disabilities or neurodivergent conditions. We therefore welcome and encourage applications from everyone, including those from groups that are under-represented in our workforce. One of our corporate objectives is to ensure that the organisational health of the firm is highly rated by our employees. We believe that this is only possible if we promote a culture of inclusion and respect across our business. Every six months we survey employees on a range of questions relating to our organisational health. This holds a mirror-up to a business and ensures that we can focus on where we need to do better. We have an Organisational Health Committee, which is chaired by a non-executive position. The panel has been formed to guide the leadership in taking positive action that supports a good work-life balance, family friendly relations and to be inviting to a diverse range of potential employees. We also have a Women in Technology Group which has been formed to promote balance in the way that we communicate with, promote, encourage, and support people across our business. 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 . click apply for full job details
We are looking for a Senior AI Engineer to join our growing Applied AI team. This is ahands-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, turningcutting-edgeresearch into practical, scalable solutions.This role reportsto the AppliedAILead. Responsibilities Lead data identification, cleaning, enrichment, preprocessing,feature engineering,andexploratoryanalysis to ensure fitness for AI workflows and to inform modelling and business decisions. Build, tune, andoptimisemachine learning, deep learning, and generative AI models,leveragingboth established andcutting-edgetechniques. Designand buildLLM-powered systems- RAG pipelines, prompt and context engineering, fine-tuning, structured outputs, function calling, context-window management,and secure model integration;selectingappropriately between standard and reasoning (test-time-compute) models, balancing capability against latency and cost. Designand buildagentic AI systems- tool-calling architectures, interoperability protocols (MCP, agent-to-agent), multi-agent orchestration, and multi-step reasoning with human-in-the-loop andappropriate trust, safety, and security boundaries. Leverageand contributeto agentic engineering tooling, including coding assistants,configurable permissions models,internal skills and plugins architectures, and sandboxed autonomous workflows integrated intoCI/CDand delivery pipelines. Applymodel adaptation techniques including parameter-efficient fine-tuning(LoRA,QLoRA), modeldistillation, and synthetic data generation for domain-specific and low-resource scenarios. Develop evaluation frameworks covering performance, hallucination, safety, cost, and non-deterministicbehaviouracross classical and generative AI systems. Optimisemodel inference and end-to-end pipelines for speed, cost, memory footprint, and scalability, including for CPU-constrained andairgappeddeployment targets. Develop andmaintainproduction-grade model pipelines and supporting software with strongMLOpspractices - covering real-time inference, batch processing, performance monitoring, drift detection, scheduled retraining, observability, governance, version control, and reproducibility. Providetechnicalguidance- 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. 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 5+ years of commercialexperience in AI and machine learning engineering and development - academic research experience is highly valued alongside this, buta track recordof commercial delivery is essential -withdemonstrabledelivery of production-grade models and systems across traditional ML, neural networks, and LLM-based systems. 5+years of commercial experience working withPython;familiaritywith 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 MScrequiredand a PhD highly desirable. Deep hands-on experience withPyTorch, Scikit-learn, and the Hugging Face ecosystem, with familiarity withMLFlowandAzureML; 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 inclassical data science fundamentals - feature engineering, statistical analysis, experimental design, and model monitoring - alongside benchmarkingand the evaluation of non-deterministic AI systems. Practical experience with inferenceoptimisationincludingquantisation, latency reduction, cost-aware model selection, and resource-efficient deployment. 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, witha track recordof owning problems end-to-end. Prior cybersecurity or business domainexpertiseis 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 Familiarity with containerisation (Docker, Kubernetes), event-driven or microservices architectures, and distributed data processing at scale. 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. Understanding of AI safety, responsible AI principles, governance, and regulatoryand standardsawareness (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. Understanding of AI applied in cybersecurity contexts, including concept drift, adversarial robustness, andmodel assurance challenges. Exposure to data engineering practices including ETL/ELT pipelines, data versioning, and schema management. Awareness of multi-modal data handling include vision, audio, or document-based inputs. 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 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. Inclusion At Glasswall we believe that diversity of people and thought are central to our purpose. We are committed to making Glasswall a company that is attractive to people of many different backgrounds. This includes diversity in every sense of the word: those with different backgrounds, ages, ethnicities, gender identities, sexual orientations, ways of thinking and those with disabilities or neurodivergent conditions. We therefore welcome and encourage applications from everyone, including those from groups that are under-represented in our workforce. One of our corporate objectives is to ensure that the organisational health of the firm is highly rated by our employees. We believe that this is only possible if we promote a culture of inclusion and respect across our business. Every six months we survey employees on a range of questions relating to our organisational health. This holds a mirror-up to a business and ensures that we can focus on where we need to do better. We have an Organisational Health Committee, which is chaired by a non-executive position. The panel has been formed to guide the leadership in taking positive action that supports a good work-life balance, family friendly relations and to be inviting to a diverse range of potential employees. We also have a Women in Technology Group which has been formed to promote balance in the way that we communicate with, promote, encourage, and support people across our business. 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 . click apply for full job details