Senior AI Engineer — Knowledge & AI Infrastructure

At Kinsworth, we aren’t just building software; we’re building a product customers love and a successful business. To achieve this, we operate at pace, we're big believers in autonomous teams and iterative development. We're crazy about continuous improvement and knowledge sharing, and we want people who share our enthusiasm.

We strive to always build the right thing and build the thing right, so we place great emphasis on understanding exactly what our customers need and delivering quality products that exceed their expectations.

We think that high-performing teams operate best through a model of build-it and run-it that comes from a deep curiosity about how our products work and how our customers use them. We use this foundation to build comprehensive product knowledge and industry expertise, but we're not dogmatic about it.

We operate in build squads that operate independently but are aligned to the same goals and ways of working. We recognise that different drives guide our roadmaps from customer needs to commercial strategy to regulatory change, and managing those dependencies is best achieved through aligned team decisions.

We make customer success everyone's business — what we are going to do and how we do it are team decisions.

We strongly believe that you grow expertise through hands-on experience, rather than just theoretical knowledge, so our approach to product leadership is designed to give you deep exposure to our products, our customers, and the fintech industry really fast. And really often.

We build our product and tech function with focus on:

• Understanding Kinsworth, the industry needs and customer use cases
• Quality is at the heart of everything we do and not just a gate in a process
• Leading product roadmaps, planning and establishing best practices
• Developing expertise in the financial services industry and regulatory environments
• Building and maintaining exceptional customer relationships through team excellence
• Zero-to-one function building — creating product delivery frameworks, engineering performance systems, and product delivery processes from the ground up
• Building and leveraging AI technology to enhance product features and development workflows

We build with technology like:

• Python & ML / LLM ecosystem — service based solutions
• Event-driven — real-time data driven solutions
• Cloud deployments — Azure, Kubernetes, ArgoCD
• High-performance data & messaging — Graph, Vector, Postgres & Kafka

About You

We’re looking for exceptional AI engineers to help build and scale Kinsworth, our AI-native financial intelligence platform.

You’ll play a central role in advancing our AI capabilities, working across applied machine learning, reasoning and decision models, and production engineering. This is a hands-on role with substantial technical ownership and the opportunity to shape a core part of the product.

You combine strong engineering fundamentals with intellectual curiosity and sound judgement. You enjoy working through complexity, evaluating new approaches and turning promising ideas into reliable software. You communicate clearly, collaborate generously and take responsibility for what you ship.

What you’ll be doing

• Design, develop and operate core AI capabilities within Kinsworth.
• Build and improve reasoning, knowledge and decision systems.
• Combine language models with structured data, computational methods and specialist models.
• Design rigorous evaluations and experiments to measure quality, reliability and performance.
• Own capabilities from data preparation and prototyping through deployment, monitoring and continuous improvement.
• Assess emerging research and technologies, applying them where they deliver measurable value.
• Work closely with engineers, domain experts and product colleagues to shape technical direction and delivery.
• Build secure, scalable and maintainable systems for Kinsworth’s financial intelligence platform.

Location

We describe ourselves as “remote but not remote only”.

We believe work can be done anywhere but relationships and trust are built best face-to-face. And that the best teams take ownership for themselves.

We strongly believe our people should decide where they will do the best work for the company. We don’t support presenteeism, nor do we support missing important face-to-face meetings to receive an Amazon delivery at home. But if you take ownership of yours and your teams’ deliveries, and will work where you need to be to get the best outcome for the company, whether that be at home, in the office in London, or meeting strategic partners, then you’re for us.

Benefits

• 25 days holiday
• Birthday day off
• Workplace Pension Scheme
• Private Medical Insurance
• Private Dental Insurance
• Group Income Protection
• Group Life Assurance
• 24-hour mental health support
• Equity Scheme (coming soon!)

Requirements

Strong experience delivering production AI/ML systems, supported by excellent software engineering fundamentals.Strong programming skills in Python and experience with typed application languages such as TypeScript.Practical experience with modern ML and LLM systems, including structured outputs, retrieval, tool use and evaluation.Strong understanding of experimental design, model limitations and systematic error analysis.Experience building and operating cloud services, including testing, observability and AI/ML operations.Ability to translate ambiguous requirements into clear technical approaches and reliable implementations.Clear communication, collaborative judgement and end-to-end ownership.Strong experience with data pipelines, transformation, quality and production data architecture.Experience with knowledge representation, entity resolution, retrieval or graph-based systems.Practical experience with document understanding and extracting structured information from unstructured data.Experience building reliable AI workflows with provenance, versioning and reproducibility.Valuable additional experienceFinancial or market data.Data governance and security in regulated environments.Temporal data, knowledge graphs or semantic modelling.Model adaptation, fine-tuning or feedback-driven improvement.Contributions to applied research or open-source engineering.