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Operational Network Engineer - CASU - CTP London Vacancy Reference Number: 20887 Number of Vacancies: 1 Agency: SO15 - Counter Terrorism Command Location: West Brompton Band: N Part/Full Time: Full Time - 36 hours per week Type of Contract: Permanent Salary: £61,410.25 (including £3,009 allowances and 12.5% shift allowance) Salary breakdown: £52,653 basic, progresses to £57,417, plus 12.5% shift allowance, £2,009 location allowance, and £1,000 non pensionable allowance. Job Summary Within Counter Terrorism Policing London (CTP London), the Covert Asset Support Unit (CASU) provides surveillance support to counter terrorism operations and deploys covert technical surveillance assets. As an Operational Network Engineer you will oversee and run the CASU network in support of CT operations, providing technical support across a broad range of network focused engineering subjects. Key Responsibilities Keep abreast of developments in relevant technologies by attending seminars, industry exhibitions, and national working groups. Identify alternative equipment options and improvements to enhance the unit's operational capabilities. Participate in an on call rota to provide operational response capability, with occasional unsociable hours. Work in difficult and challenging environments subject to appropriate risk assessment and mitigation. Interact with members of the public professionally while maintaining confidentiality. Drive MPS vehicles as required; no company car provided. Maintain a physical presence at a central London site with flexible shift pattern (). Remote work not permitted. Handle sensitive and classified information within a covert environment. Key Skills & Qualifications Degree, HNC, HND, or higher education in relevant subject, or equivalent demonstrable experience as a network engineer with industry recognised certification. Good understanding and experience of network monitoring and troubleshooting tools. Proficiency with network security and firewall technologies. Knowledge of IP networks and configuration/administration. Understanding of video and audio standards, compression, recording (NVRs), and mass storage of data and video monitoring systems. Recognised engineering training such as apprenticeship, traineeship, or armed forces trade training. Demonstrable project management and delivery experience. Minimum two years of relevant post training experience. Experience Two years of post training, relevant experience as a network engineer, including diagnosing network issues and performing day to day maintenance with minimal mentoring. Desirable Experience with Cisco and Juniper networking equipment and VPN technologies. Experience with virtual machines, network design, and distribution of audio and video feeds over IP networks. Previous work in a law enforcement or military technical surveillance unit. Operational experience deploying audio, video, tracking, and associated transmission equipment. Knowledge of transmission systems: radio frequency, modulation, antennas, IP radio, mobile data networks, and satellite. Held or previously held Developed Vetting level security vetting. Vetting Applicants must hold or be prepared to undergo National Security Vetting (NSV) - Developed Vetting (DV) level before taking up the post. Applicants must be British nationals (some dual nationals may be ineligible) and possess an Enhanced Security Check (eSC)/Developed Vetting (DV) clearance. Travel to certain countries may be limited; additional security checks may be required. Confidentiality Agreement Applicants will be required to sign a confidentiality agreement upon taking up the post.

Multiverse is the upskilling platform for AI and Tech adoption. We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today's workforce. Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they've learned to improve productivity and measurable performance. In June 2022, we announced a $220 million Series D funding round co-led by StepStone Group, Lightspeed Venture Partners and General Catalyst. With a post money valuation of $1.7bn, the round makes us the UK's first EdTech unicorn. But we aren't stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We're building a world where tech skills unlock people's potential and output. Join Multiverse and power our mission to equip the workforce to win in the AI era. The Role Multiverse is the UK's largest apprenticeship provider and its first EdTech unicorn. The current state of AI presents a huge opportunity to reshape the future of education and workforce development. Multiverse is in a uniquely strong position to do that, and getting it right has implications beyond the company: for the UK tech sector and the broader economy. The AI Transformation team exists to make that real, starting with Multiverse itself. This is not a team that bolts AI onto the edges of the business or ships a handful of internal productivity tools. The mandate is bigger: to rebuild how the company actually works, function by function, and to establish the practices that make Multiverse an AI first company from the core out. That work matters twice over. Get it right inside Multiverse and we move faster, serve learners better, and operate at a level few organisations can match. But Multiverse also exists to build the workforce that every other company is reaching for. The way we transform ourselves becomes the standard we set for everyone else. You are not just changing one company, you are building the blueprint others will follow. The team is one small, focused squad, accountable for outcomes end to end. You work closely with the wider engineering org building Multiverse's customer facing product, and alongside the teams whose work you are helping to reinvent. The structure is flat and fast. No shared queues, no bureaucratic overhead between having an idea and shipping it. Whilst we are building something entirely new, Multiverse has an established product, existing infrastructure, and engineering teams in London and Berlin. You need to be as comfortable integrating existing systems and working across team boundaries as you are building new ones from scratch. What You Will Do Own and deliver complete agent systems. You take a product problem and build the agent system that solves it. Architecture, implementation, evaluation, and production operation. You are responsible for the system working, not just for your code compiling. Design context and retrieval strategies. What goes into the context window and what stays out is the most consequential design decision in an AI system. You design retrieval pipelines, conversation memory, summarisation strategies, and the chunking logic that makes context useful rather than noisy. You understand the cost and quality trade offs at every layer. Build evaluation frameworks. You define and implement the metrics that tell the team whether its AI systems are doing what they should. Accuracy, safety, helpfulness, domain specific quality, latency. You build automated eval pipelines and human in the loop review processes. You treat evaluation as an engineering discipline, not an afterthought. Design tool integrations. Agents are only as capable as the systems they can reach. You design and build the tool layer: MCPs, APIs, data contracts, and the error handling that makes tool use reliable. You work closely with the wider engineering org building Multiverse's customer facing product, whose systems your agents need to interact with. Influence technical direction. You have opinions about how things should be built, and you back them up with evidence. You contribute to architectural decisions, push back when the team is heading in the wrong direction, and propose better approaches. You are not a team lead, but your technical judgement shapes what gets built and how. Raise the bar through code review and pairing. You review code with rigour and give feedback that makes the team better. You pair with less experienced engineers on hard problems. You set a standard for what production quality AI engineering looks like. Use Claude Code as your primary development workflow. Claude Code is how this team builds. You set context, define constraints, review output critically, and augment the tool with skills and domain context. You are fluent in AI assisted development and can mentor others in doing it well. What We Are Looking For Production AI Agent Engineering Context management. Designing what enters the context window and what stays out. Retrieval strategies, chunking approaches, conversation memory, summarisation. You know how context quality drives output quality and cost, and you have made these trade offs in production. Model selection and routing. Choosing the right model for a task based on capability, latency, cost, and reliability. You have worked with multiple models and understand when a smaller, faster model is the right call. Cost engineering. Token economics, caching, prompt optimisation, batching. You know the difference between a prototype that works and a production system that works at a cost the business can sustain. Tool use and agent augmentation. Designing the tool surfaces that agents use to interact with external systems. Writing tool descriptions that models use reliably, handling failures gracefully, building integration layers that are composable rather than brittle. Evaluation. Building frameworks for assessing AI output quality: accuracy, safety, helpfulness, domain specific criteria. You ship with eval, not after it. Product Thinking You do not wait for a spec. You understand the problem, figure out what needs to exist, and build it. On a small squad there is no gap between product thinking and engineering. You talk to users, understand their workflows, and identify the highest value intervention. This does not require product management experience. It requires the instinct to ask "what problem are we solving and for whom?" before "what framework should we use?" Full Stack Delivery You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end. Agent systems do not fit neatly into service boundaries, and your ability to work across all of them is a practical requirement. Communication You explain technical decisions clearly to both engineers and the product and design people you work with day to day. You document your designs, write pull requests that tell a story, and give direct feedback without being abrasive. What Would Set You Apart Experience building AI systems in EdTech, regulated content, or domains where output quality has compliance or accreditation implications Background as a founding or early stage engineer at a startup Published thinking or external contributions in AI engineering (talks, writing, open source) Experience with multi agent coordination: task decomposition, handoff, shared state Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards What We Are Not Looking For Pure ML researchers without production engineering experience. We build products, not papers Narrow specialists. If you only do infrastructure, or only do model training, or only do frontend, this team needs broader range Engineers who need a detailed spec and a sprint plan before starting. We ship fast and iterate Candidates whose AI experience stops at wrapping LLM APIs. We need depth in context strategy, evaluation, tool design, and the systems engineering underneath Engineers who optimise for technical elegance over user outcomes. The architecture serves the product Benefits Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company wide wellbeing days (M Powered Weekend) and 8 bank holidays per year Health & Wellness - private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Wellhub and access to Spill - all in one mental health support Hybrid work offering - for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month Work from anywhere scheme - you'll have the opportunity to work from anywhere, up to 10 days per year Space to connect: Beyond the desk, we make time for weekly catch ups, seasonal celebrations, and have a kitchen that's always stocked! Our Commitment to Diversity, Equity and Inclusion We're an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship . click apply for full job details

Multiverse is the upskilling platform for AI and Tech adoption. We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today's workforce. Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they've learned to improve productivity and measurable performance. In June 2022, we announced a $220 million Series D funding round co-led by StepStone Group, Lightspeed Venture Partners and General Catalyst. With a post money valuation of $1.7bn, the round makes us the UK's first EdTech unicorn. But we aren't stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We're building a world where tech skills unlock people's potential and output. Join Multiverse and power our mission to equip the workforce to win in the AI era. The Role Multiverse is the UK's largest apprenticeship provider and its first EdTech unicorn. The current state of AI presents a huge opportunity to reshape the future of education and workforce development. Multiverse is in a uniquely strong position to do that, and getting it right has implications beyond the company: for the UK tech sector and the broader economy. The AI Transformation team exists to make that real, starting with Multiverse itself. This is not a team that bolts AI onto the edges of the business or ships a handful of internal productivity tools. The mandate is bigger: to rebuild how the company actually works, function by function, and to establish the engineering practices that make Multiverse an AI first company from the core out. That work matters twice over. Get it right inside Multiverse and we move faster, serve learners better, and operate at a level few organisations can match. But Multiverse also exists to build the workforce that every other company is reaching for. The way we transform ourselves becomes the standard we set for everyone else. You are not just changing one company, you are building the blueprint others will follow. The team is one small, focused squad, accountable for outcomes end to end. You work closely with the wider engineering org building Multiverse's customer facing product, and alongside the teams whose work you are helping to reinvent. The structure is flat and fast. No shared queues, no bureaucratic overhead between having an idea and shipping it. Whilst we are building something entirely new, Multiverse has an established product, existing infrastructure, and engineering teams in London and Berlin. You need to be as comfortable integrating existing systems and working across team boundaries as you are building new ones from scratch. What You Will Do Own the architecture of our internal agentic operating system. The team's work spans the full surface of how Multiverse operates. You own the technical architecture of our agentic operating system: the agent orchestration, context strategy, tool integrations, evaluation framework, and production operation. Your design decisions shape what is possible for human and AI teams at Multiverse. Ship production AI agent systems. This is a building role. You write code, review code, and own the quality of what goes to production. You will personally build and deliver significant agent systems. On a squad this size, nobody leads from a whiteboard. Design multi-agent coordination. Task decomposition across agents, handoff protocols, shared state management, orchestration logic. You know the difference between agents that genuinely coordinate and agents that run sequentially and hope for the best. You design the patterns that make multi-agent systems reliable. Build the evaluation and quality infrastructure. Automated eval pipelines, human in the loop review systems, regression testing for prompt changes, domain-specific quality metrics. You treat evaluation as a first class engineering concern and build the systems that make it possible at scale. Drive cost engineering. Token economics, caching strategies, model routing, prompt optimisation. The cost profile of production AI systems requires active engineering attention, and you build the cost awareness and tooling into the architecture rather than bolting it on later. Build the integration layer that makes existing Multiverse systems agent accessible. APIs, MCPs, shared data contracts, and the tooling that connects agents to the platform, content systems, and the tools the company runs on. This means building real working relationships with engineering teams across London and designing interfaces that serve both sides well. Set the standard. You define patterns for prompt management, retrieval, guardrails, and testing that the wider team and eventually the whole organisation adopts - and that, in time, shape how the companies who learn from Multiverse do this too. You do this through code, documentation, and architectural decisions, not through mandates. Mentor the team. Code review, architectural guidance, pairing on the hardest problems. You are not a line manager, but your technical leadership directly shapes the growth of the engineers around you. What We Are Looking For Production AI Agent Engineering You have shipped multi agent systems or complex AI products to real users. You understand the engineering challenges that make agent systems a distinct discipline: Context management. Designing what enters the context window and what stays out. Retrieval strategies, chunking, conversation memory, summarisation, and the cost/quality trade offs of each. You have made these decisions in production and seen the consequences. Model selection and routing. Choosing the right model for each task based on capability, latency, cost, and reliability. Building routing logic that matches work to the appropriate model rather than defaulting to one. Cost engineering. Token economics, caching, prompt optimisation, batching. You know the difference between a prototype that works and a production system that works at sustainable cost. You have built systems where cost was an engineering constraint, not someone else's problem. Tool use and agent augmentation. Designing what capabilities agents can reach: tool descriptions that models use reliably, failure handling, MCPs or equivalent interfaces. You understand that the quality of the tool layer determines whether agents are useful or fragile. Multi agent coordination. Task decomposition across agents, handoff protocols, shared state, orchestration logic. You have built systems where multiple agents work together within a product domain and understand the architectural patterns that make coordination reliable. Evaluation and quality. Building eval frameworks for AI output: accuracy, helpfulness, safety, domain specific criteria. Automated pipelines and human in the loop review. You would not ship an agent system without a quality baseline. Product Thinking and Entrepreneurial Instinct On a small squad there is no gap between product thinking and engineering. You own the problem from user need to production system. You can sit with the people whose work you are transforming, understand their workflow, identify the highest value intervention, and build it without waiting for a product manager to write a spec. You have either built something yourself (a product, a startup, a project with real users) or operated with that founder mindset inside a larger organisation. You understand that speed matters and that shipping something useful beats polishing something theoretical. AI Native Engineering You build with Claude Code daily. You set context and constraints before generating code. You review AI output critically. You augment the tool with skills, system prompts, and domain context to make it effective. This is how the team works, and you help define what good looks like. Full Stack Delivery You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end. The boundaries between these layers dissolve in agent systems, and so should your willingness to work across them. Communication You can explain technical strategy to a CPO, walk a product manager through a cost trade off, and give direct feedback in code review. You represent the team's technical approach in cross functional forums with product, design, learning design, compliance, and other engineering teams. You document decisions, not just code. What Would Set You Apart Experience in EdTech, regulated content, or domains where AI output quality has compliance or accreditation implications. Background as a founding engineer or technical co founder. Published thinking or external contributions in AI engineering (talks, writing, open source). Experience designing platform layers that other teams build on. Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards. What We Are Not Looking For Pure ML research without production engineering experience. We need builders. Narrow specialism. This team works across the full stack of an AI product. If you only do infrastructure, or only do model training, or only do frontend, this is the wrong fit. People who need a detailed spec, a sprint plan, and a standup before they can write a line of code . click apply for full job details

Project Engineer (Senior Agent) - Power (Substation) Location: Bridgend, South Wales Sector: Power / Major Civils Infrastructure Type: Full-time, Permanent Be part of one of the largest upgrades to the UK power network in decades - a transformative, high-value project that will shape the nation's energy future. We're looking for a Project Engineer to take the lead on complex technical set up and delivery of critical infrastructure in the Power sector. If you're passionate about delivering technical excellence, driving safety, and managing major infrastructure at scale, we want to hear from you. You'll provide technical construction of the civil engineering of a GIS Substation. Supported by a team of Engineers you will deliver; concrete sub-structure, steel support, steel frame & cladding and non-HV mechanical and electrical works. Key Responsibilities: Support the Project Lead with complex workstream to successful delivery on time, within budget and to exacting safety and quality standards Champion safety - supporting a zero-tolerance approach to unsafe practices Collaborate with planning teams to produce detailed short-term programmes Drive technical delivery across staff and contractors Oversee budgets and resource requirements; ensure accurate progress reporting Liaise with clients and design partners on ECI works for buildability and construction methods Ensure scope of works are developed from design information for procurement and pricing to be undertaken Support Digital build, DFMA and Engineered Safety Essential Skills & Experience: Proven track record of delivering major infrastructure projects (Power or Civils) Strong HSE management and leadership capabilities Deep understanding of project delivery lifecycle - scope, schedule, cost, and quality Effective contractor management and construction knowledge Quality management expertise and attention to technical detail Temporary Works Knowledge Design & Build experience Degree in Civil Engineering or similar qualification, including apprenticeship routes, and on-the-job experience Why Join This Project? Stability and very long-term work Work on one of the most significant energy upgrades in UK history Career-defining opportunity with high visibility and impact Supportive, forward-thinking team culture Strong focus on safety, sustainability, and innovation About us: We are an international engineering and construction company delivering state-of-the-art infrastructure and buildings projects for clients in the UK, Middle East and Australia. Certainty, reliability, quality - this is what our clients want. And at Laing O'Rourke, we have more than 150 years of experience delivering it. Laing O'Rourke's story is one of energy, passion, ambition, people and teamwork. We harness the power of our experience, stretching back over a century and a half to deliver certainty for our clients. We want to ensure our recruitment process is accessible to all. If you need the application form in an alternative format or you would like to know more about our recruitment process, please email .