- Senior
- Optionales Büro in Warwick
About the Role
At Bright, we're transforming how accountants and payroll professionals work. We're not building AI for AI's sake - we're creating intelligent automation workflows that solve real problems, turning hours of manual work into minutes of intelligent automation.
As our AI Workflow Manager, you'll build and lead a team of 7-10 talented graduate AI engineers while staying hands-on with the work that matters most. This is a genuine player-coach role where you'll spend half your time developing people (1-on-1s, code reviews, career guidance) and half your time building production systems alongside them using tools like Langflow.
You'll be shaping a team from the ground up - establishing engineering standards, building a culture of quality, and watching graduates develop into strong engineers. When your team needs deep ML expertise, you'll partner with our experienced AI Platform team. Your focus is on delivery, production excellence, and growing your team's capabilities.
This role reports to the Head of AI and sits at the heart of collaboration between AI Platform, Product, and Engineering teams.
Key Responsibilities
Leading Your Team (50%)
- Building People You'll manage 7-10 graduate AI engineers, helping them grow from early career to confident contributors. Weekly 1-on-1s are your tool for understanding blockers, providing guidance, and supporting their development. You'll set clear expectations, give honest feedback, and create space for people to learn and experiment.
- Establishing Standards You'll define what "good" looks like - code quality standards, testing and evaluation practices, documentation requirements. Through thorough code reviews and ensuring robust evaluation, you'll teach best practices and catch issues before they reach production. Your reviews are teaching moments, not gatekeeping.
- Managing Delivery You'll break down complex automation projects into clear tasks, assign work thoughtfully based on skills and growth goals, and keep things moving. When graduates need ML guidance (model training, evaluation), you'll coordinate with the AI Platform team. You'll also manage the less glamorous but critical work: dependencies, CVEs, technical debt.
- Reporting Up You'll keep leadership informed on team progress, capacity, and what you need to succeed.
Building Production Systems (50%)
- Designing Workflows You'll architect production-grade workflow automations using Langflow that integrate AI capabilities - LLMs, document processing, entity extraction - into reliable business processes. These aren't demos; they're systems our customers depend on daily.
- Integrating Systems You'll build complex API integrations connecting internal systems with external services. You understand OAuth flows, rate limiting, retry strategies, and the reality of systems that sometimes fail.
- Writing Code When off-the-shelf components aren't enough, you'll develop custom Python components. You'll establish patterns for error handling, monitoring, logging, and testing that the team can follow.
- Ensuring Reliability You own production reliability for your team's workflows. When things break (hopefully rarely), you'll have built systems that fail gracefully and alert appropriately.
- Leading by Example Your code and designs set the standard. When team members look at your work, they see what great looks like.
Skills, Knowledge and Expertise
You Need
- Management Experience You've managed technical teams for 3+ years and are comfortable with the reality that managing 7-10 people is demanding work. You know how to have difficult conversations, how to motivate different personalities, and how to balance being supportive with holding high standards.
- Technical Depth You've been writing Python professionally for 5+ years and have strong software engineering fundamentals. You're an expert at code reviews - you know how to give feedback that improves both the code and the engineer. You've built production workflows with tools like Langflow, n8n, Airflow, or Prefect and dealt with the messy reality of integrating systems that weren't designed to work together.
- You understand distributed systems well enough to build reliable async workflows. You're comfortable with Git beyond the basics - branching strategies, resolving merge conflicts, maintaining clean history. You can write SQL that performs well and understand when to add an index.
- AI Integration Experience You've integrated AI/LLM APIs into production systems and understand both their capabilities and limitations. You know when to use AI and when traditional logic is better. You understand the ML project lifecycle well enough to manage people doing ML work and collaborate effectively with ML experts. You're aware of concepts like NLP, OCR, entity extraction, and text classification, even if you're not building these from scratch.
- Communication Skills You can explain complex technical concepts clearly to both engineers and stakeholders. You write documentation that people actually want to read.
Nice to Have
Experience shipping AI/ML projects, working with NLP technologies in production, Supabase, Kubernetes, Azure, Docker, CI/CD pipeline design (GitHub Actions), vector databases, observability platforms (Prometheus, Grafana, Langfuse), or JavaScript/TypeScript for custom integrations.
What Makes You Right for This
You enjoy the challenge of managing people as much as solving technical problems. You get genuine satisfaction from watching a graduate engineer have an "aha" moment or ship their first production feature.
You build production systems properly from day one - error handling, monitoring, logging, graceful degradation. You're pragmatic about AI: you understand what LLMs can and can't do, and you make decisions based on production realities, not hype.
You establish quality standards that lift everyone's work. You lead by example - your technical credibility comes from doing the work, not just directing it. You understand that robust evaluation is what separates workflows that work from workflows that seem to work.
You're comfortable partnering with the AI Platform team for deep ML expertise rather than needing to be the expert in everything.
The Team You'll Build
You'll report to the Head of AI and directly manage 7-10 graduate AI engineers. You'll work closely with our AI Platform engineers (who can help with ML guidance), application developers, and product managers.
Your team will build and maintain AI-enhanced workflow automations that power everything from document processing to employee onboarding to customer support intelligence.
How We'll Know It's Working
After 3 months, you'll have established effective 1-on-1 rhythms with everyone, understood what the team is capable of and where they need to grow, shipped at least one significant workflow, and built good working relationships with the AI Platform team.
After 6 months, your team will be delivering predictably, code quality will have noticeably improved, you'll be delegating effectively while staying hands-on where it matters, team members will be taking on more complex work, and production incidents will be decreasing.
After 12 months, your team will have delivered multiple high-impact automation projects, at least 2-3 engineers will have grown significantly, you'll have built reusable patterns that accelerate future work, your team will have a reputation for quality and reliability, and you'll be contributing to strategic planning for Bright's AI capabilities.
What You'll Work With
Core tools: Langflow, Python, Supabase, Langfuse
Integration: REST APIs, webhooks, OAuth 2.0
AI Services: LiteLLM Proxy, Azure OpenAI, Google Vertex
Data: PostgreSQL, Azure Blob Storage
Observability: Langfuse, Prometheus, Grafana, Loki
Development: Git, GitHub, VS Code
Deployment: Kubernetes (AKS), Docker, ArgoCD
Integration: REST APIs, webhooks, OAuth 2.0
AI Services: LiteLLM Proxy, Azure OpenAI, Google Vertex
Data: PostgreSQL, Azure Blob Storage
Observability: Langfuse, Prometheus, Grafana, Loki
Development: Git, GitHub, VS Code
Deployment: Kubernetes (AKS), Docker, ArgoCD
Why This Role Matters
You'll be building a high-performing engineering team from the ground up. You'll help establish standards, choose approaches, and shape the team's culture. You'll stay technically sharp while developing leadership skills, working on cutting-edge AI applications that solve real problems.
You'll work in a collaborative environment where an experienced AI Platform team supports your team's learning. And the impact is tangible - you'll see accountants and finance professionals working more efficiently because of the systems you and your team built.
This is a chance to shape both technology and people, to balance hands-on technical work with genuine leadership, and to build something that matters.
Benefits
What will you get?
- Competitive salary
- Performance based bonus
- 25 days annual leave
- Health Insurance
- Company pension
- Company events
- free food onsite
- On-site parking
- Referral programme
- Sick pay
- Wellness programmes
