AI Workflow Intelligence Scale-Up (London) - Xist4
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AI Workflow Intelligence Scale-Up (London)

Hiring a Senior Full-Stack Engineer to Build and Scale an AI-Native Workflow Insights Platform



The Challenge

Our client, a fast-growing London SaaS scale-up, was building an AI-native workflow intelligence platform used by large enterprises to understand, optimise, and automate complex operational processes across compliance, customer operations, finance, and risk. The platform connected the systems, steps, and interactions that underpin critical workflows, allowing organisations to identify bottlenecks, reduce cycle times, and maintain evidence-based operational control.

Following a successful Series B round, the business was entering an accelerated scale-up phase. Their engineering team needed to evolve the platform from supporting dozens of enterprise teams to supporting hundreds of global customers, each requiring secure, scalable, real-time workflow intelligence and AI-driven insights.

To support this expansion, the company needed a Senior Full-Stack Engineer with deep experience in Typescript, React, AWS serverless technologies and event-driven architecture. They needed someone who could work across the stack, contribute to product shaping, and design infrastructure to support high-volume workflow analytics in regulated environments.

The challenge was identifying engineers who combined hands-on full-stack capability with a product mindset, cloud-native architectural depth, and experience delivering end-to-end features at pace. The leadership team also needed market intelligence on compensation, stack alignment, and available talent pools before scaling their engineering organisation.

The Solution

Xist4 delivered a research-led technical search, underpinned by its Recruitment Insight Audit Report, to provide a clear, data-backed understanding of senior engineering talent in the UK AI and workflow automation ecosystem.

Our approach included:

  • Defining the Success Profile: Worked with the CTO and Product Lead to refine the technical and product competencies required, covering Typescript, React, Material UI, serverless architecture (AWS Lambda, EventBridge, DynamoDB), Hasura, Postgres, SST, CDK, and end-to-end ownership.

  • Recruitment Insight Audit: Produced a market report profiling over 150 senior engineers with experience in cloud-native platforms, workflow automation, event-driven systems, and AI-backed enterprise SaaS. The audit included salary bands, talent availability by region, stack alignment, and competitor hiring velocity.

  • Talent Mapping and Engagement: Identified engineers from scale-ups in process intelligence, workflow automation, and operational analytics. Targeted outreach positioned the company as a purpose-driven AI workflow platform where engineers have significant ownership and influence over core systems.

  • Structured Candidate Assessment: Conducted technical interviews evaluating system design skills, event-driven architecture capability, cloud-native design choices, and ability to deliver in a fast-moving product environment. Product thinking, collaboration, and problem-solving were evaluated equally alongside technical depth.

  • Weekly Market Intelligence: Shared insights on candidate supply, salary expectations, response rates, and perceived role attractiveness, enabling the leadership team to refine their hiring strategy with confidence.

The Result

Within seven and a half weeks, Xist4 presented a high-quality shortlist of senior full-stack engineers with strong experience in Typescript, React, AWS serverless, and event-driven architecture.

The appointed candidate had previously scaled workflow and analytics features at a well-known enterprise SaaS vendor. They brought hands-on expertise across the company’s exact stack, including SST, CDK, DynamoDB, Lambda, Postgres, and React. They quickly took ownership of the workflow ingestion engine and contributed to redesigning the system to support significantly higher concurrency and throughput.

Within their first quarter, the engineering organisation reported a measurable improvement in shipping velocity, enhanced feature reliability, and a stronger foundation for expanding AI-driven workflow insights.

This engagement demonstrated how combining market intelligence with precision-driven technical search helps AI-native scale-ups secure the senior engineering talent needed to transition from early product traction to enterprise-grade maturity.

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