Rowden

Edge computing for defence and security

ML Solutions Architect

Pre-Sales Solution EngineeringUpdated on Nov 13

Full-timeBristol, UK£70k - £100k /yrHybrid

About the Job

Requirements

Hands-on experience as an AI or ML engineer, including building, training, and deploying models.
Working knowledge of at least one of agentic AI and LLM application patterns, ML model serving and monitoring, data pipeline design, or MLOps practices.
Ability to consider why a system is being built and how it fits into the wider system, in addition to implementation.
Ability to explain technical concepts clearly to people without deep technical expertise.
Curiosity and willingness to develop knowledge outside the candidate's current specialism.
Awareness of AI security and safety principles, including secure data handling, access control, and model risk, and willingness to develop this further.
Eligibility for SC clearance.

Responsibilities

Support the design of end-to-end AI pipelines, including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring, contributing technical input and developing architectural judgement under senior guidance.
Assist in designing and documenting agentic AI systems, including tool use, orchestration, memory and context management, and LLM integration patterns.
Support integration of AI and ML solutions into wider systems, working with engineering teams to connect AI components reliably with existing platforms and applications.
Develop knowledge of the MLOps lifecycle, including retraining cadences, model versioning, drift detection, and rollback procedures, to support robust production AI systems.
Contribute to technical proposals, architecture diagrams, and solution documentation for bids and pre-sales engagements, working with senior architects.
Research and assess tooling, platform, and hosting options, summarising trade-offs to inform architectural decisions.
Support identification of technical risks in AI solutions, including data quality issues, model drift, integration failure modes, and AI security and safety considerations.
Work with ML engineers, data engineers, and domain subject-matter experts to understand specialist constraints and incorporate them into solution designs.
Develop understanding of how AI solutions are scoped, costed, planned, and assured across the programme lifecycle.
Gradually take ownership of discrete solution components as confidence and experience grow.

Desired Qualifications

Exposure to data platform patterns such as medallion or lakehouse architectures.
Hands-on experience with MLOps tooling or model-serving infrastructure used to operationalise and scale ML systems.
Experience contributing to technical documentation, proposals, or solution designs, including informal contributions.
Familiarity with edge deployment constraints or resource-limited inference environments.
Experience in secure, regulated, or government-adjacent environments.

We're building the UK's next generation engineering powerhouse, providing critical technology that strengthens national security and resilience.

We specialise in turning advances in sensing, AI, and communications into operational capability for the edge, where connectivity may be degraded or denied. Our work focuses on accelerating the deployment of technology, improving decision-making for frontline teams, and protecting people and critical assets in demanding environments.

Headquartered in Bristol, Rowden employs around 200 people and operates over 20,000 square feet of engineering and manufacturing facilities. We have a growing international footprint and are one of Europe's fastest-growing engineering businesses.

About the role

We are looking for an ML Solutions Architect to join our growing ML capability, designed for a capable AI or ML engineer who wants to move beyond pure implementation and start thinking at a systems and solution level.

You will work alongside senior architects on live programmes and pre-sales engagements. You will get structured exposure to the full arc of solution architecture, from shaping problem statements and designing AI pipelines through to supporting bids and engaging with customers, with direct mentorship and increasing ownership over time.

This is not a research role or a pure engineering role. It suits someone who is technically strong, curious about how AI systems fit into larger operational contexts, and ready to start thinking about how solutions are designed, communicated, and delivered, not just built.

Candidates must be eligible for SC clearance.

More information about security clearance is available here: https://www.gov.uk/government/publications/united-kingdom-security-vetting-clearance-levels

Support the design of end-to-end AI pipelines, from data ingestion and feature engineering through to model training, evaluation, deployment, and monitoring, contributing technical input and developing architectural judgement under senior guidance.
Assist in the design and documentation of agentic AI systems, including tool use, orchestration, memory/context management, and LLM integration patterns.
Support the integration of AI & ML solutions into wider systems, working with engineering teams to ensure AI components connect reliably with existing platforms and applications.
Develop in-depth knowledge of the MLOps lifecycle, including retraining cadences, model versioning, drift detection, and rollback procedures, to support robust and reliable production AI systems.
Contribute to technical proposals, architecture diagrams, and solution documentation for bids and pre-sales engagements, working closely with senior architects.
Research and assess tooling, platform, and hosting options, summarising trade-offs clearly to inform architectural decisions.
Support the identification of technical risks in AI solutions, including data quality issues, model drift, integration failure modes, and AI security and safety considerations.
Work with ML engineers, data engineers, and domain SMEs to understand specialist constraints and incorporate them into solution designs.
Develop your understanding of how AI solutions are scoped, costed, planned, and assured across the programme lifecycle.
Gradually take on more ownership of discrete solution components as confidence and experience grow.

We are looking for someone with solid AI and ML engineering foundations who is ready to start applying that knowledge at a higher level. We value potential and appetite as much as prior experience in architecture.

Essential

Hands-on experience as an AI or ML engineer. You have built, trained, and deployed models and understand what makes AI systems work in practice.
Working knowledge of at least one of: agentic AI and LLM application patterns, ML model serving and monitoring, data pipeline design, or MLOps practices.
Ability to think beyond implementation. You can ask “why are we building this?” and “how does this fit into the wider system?” as well as “how do we build it?”.
Clear written and verbal communication. You can explain technical concepts to people who aren’t deeply in the weeds.
Curiosity and a willingness to develop in areas outside your current specialism.
Awareness of AI security and safety principles (e.g. secure data handling, access control, model risk), and a willingness to build this further given Rowden’s defence and national security context.
Eligibility for SC clearance.

Desirable (not essential)

Exposure to data platform patterns such as medallion or lakehouse architectures.
Hands-on experience with MLOps tooling or model-serving infrastructure used to operationalise and scale ML systems.
Any experience contributing to technical documentation, proposals, or solution designs, even informally.
Familiarity with edge deployment constraints or resource-limited inference environments.
Experience in secure, regulated, or government-adjacent environments.

About you

You are technically grounded but starting to think beyond the code. You want to understand the full shape of a solution, not just your part of it.
You communicate clearly and are comfortable articulating ideas to people at different levels.
You are curious and self-directed. You don’t wait to be told what to learn next.
You are humble about what you don’t know, and you actively seek to close those gaps.
You want a role where you are developed, not just deployed.
You are ready to engage with customers and stakeholders, even if that’s new territory, and you see it as an opportunity.

About the Company

Rowden

Edge computing for defence and security

Rowden Technologies designs and delivers mission-critical edge computing and distributed sensor systems for environments with limited or unreliable connectivity. It handles end-to-end design, integration, sovereign manufacturing, and long-term managed services to support defence, national security, and emergency services. Its products include Trove for frontline generative AI, Magos for cost-effective signal processing and communications production, and Dovetail for modular edge-system integration, enabling rapid, secure decision-making even in denied environments. The company differentiates itself by prioritizing edge solutions with low connectivity, acting as a prime systems integrator for large programmes, offering sovereign manufacturing and lifecycle support, and pursuing projects like automated ML deployment to UAS for AUKUS Pillar II to reduce dependence on traditional defence contractors.

Company size: 51-200
Company stage: Grant
Total funding: $33.7M
Headquarters: Filton, United Kingdom
Founded: 2017

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