IT Project Manager Contractor Payments

London (Bromley/St Paul's) | Hybrid Working
12-Month Contract
£900/day PAYE

Our client is a well-established consulting and technology partner operating across the financial services sector, supporting some of the world's largest banking institutions.

They are currently seeking a Senior Python Engineer to join a major global investment bank's Equities and Risk technology organisation. This is a hands-on engineering position focused on building and enhancing software that underpins front-office trading activity. The team is looking for a strong software engineer with a proven track record delivering production systems within investment banking environments.

Experience with platforms such as Quartz, Athena, SecDB or Beacon would be particularly relevant, as would previous exposure to Equities, Derivatives, Risk or Electronic Trading technology.

What we're looking for

• Strong commercial Python development experience
• Background delivering complex software within investment banking, capital markets or electronic trading environments
• Experience building and supporting production trading platforms
• Strong understanding of software architecture, APIs and distributed systems
• Ability to work closely with front-office stakeholders and translate business requirements into robust technical solutions
• Experience with Quartz, Athena, SecDB or Beacon is highly desirable

Additional Information

• 12-month contract
• £900/day PAYE
• Hybrid working with a couple of days per week onsite in Bromley or St Paul's
• Applicants must have recent banking experience

If you're a senior engineer from a banking background who enjoys solving complex technical problems within trading environments, we'd be happy to discuss the opportunity in more detail.

 Senior Machine Learning Engineer (AI / LLM Systems)

Remote (United Kingdom) | Permanent | Flexible compensation + equity

We’re working with a well-established, tech-led business that is building a new AI product focused on real-world tasks, workflows, and decision-making.

This is a small, high-calibre team building systems where model capability is transformed into reliable, production-grade ML systems, with a strong emphasis on ownership, iteration, and real-world performance.
The product focuses on:

• Long-running AI workflows

• Persistent context across interactions

• Multi-step reasoning and task execution

• Integration with external tools and systems

The core challenge is designing ML systems that can behave reliably in production, even when model outputs are inherently non-deterministic.

The Role

This role sits at the core of the ML layer powering the product.
The focus is on designing and operating systems that enable:

• ML models to run reliably in production

• End-to-end pipelines from training to inference

• Continuous evaluation and iterative improvement

• Systems that perform consistently under real usage conditions

You’ll be working on:

• Training, inference, and evaluation pipelines

• LLM-based systems and agent-style workflows

• Debugging model behaviour using real-world signals

• Optimising performance across latency, cost, and reliability

• Production monitoring, logging, and system stability

What They Care About

The hiring bar is centred around real production ML experience:
Whether you have:

• Shipped ML systems used by real users

• Owned ML systems end-to-end in production

• Worked with modern LLMs beyond simple API integration

Your exposure to practical challenges such as:

• Model behaviour debugging and failure analysis

• Latency, throughput, and cost optimisation

• Monitoring, observability, and evaluation frameworks

• Scaling ML systems in production environments

Tech Environment

• Python (core ML and backend language)

• PyTorch / modern ML frameworks

• LLM ecosystem (OpenAI, Anthropic, etc.)

• GPU-based training and inference

• Docker and Kubernetes

• AWS, Azure or GCP

The emphasis is on how you build and operate ML systems, rather than specific tools.

Team & Working Style

• Fully remote-first, work from anywhere

• Small, highly capable engineering team

• Strong emphasis on ownership and delivery

• Fast iteration cycles with real user feedback

• Comfortable working in evolving systems and making pragmatic decisions 

 Full Stack Engineer (AI Systems) – Remote (Global)

We’re working with a well-established, tech-led business that is building a new AI product focused on handling real-world tasks, workflows and decision-making.

This is a small, high-calibre team building systems where AI capability is translated into reliable, structured product behaviour, with a strong emphasis on execution and consistency in real usage.

The product focuses on:

• Long-running workflows
• Persistent context
• Multi-step task execution
• Integration with external systems
• The core challenge is designing systems that can apply model capability in a structured and reliable way, even when behaviour is non-deterministic.

The Role

This role sits at the product layer, connecting backend systems, AI capability and the end-user experience.

The focus is on building systems that enable:

• AI workflows to run end-to-end within the product
• User-facing features that behave consistently under real usage
• Real-time interaction with AI-driven systems

You’ll be working on:

• End-to-end product features across frontend and backend
• Agent workflows, including planning, tool usage, failure handling and recovery
• Integration of LLMs, memory and external systems into user-facing flows
• Real-time interactions, including streaming responses and partial updates
• Reliability and fallback behaviour within the product experience

What They Care About

The hiring bar is centred around real production experience:

• Whether you have built and shipped features end-to-end
• Your experience working with AI-powered applications beyond simple API usage
• Your ability to handle practical challenges such as:
• ambiguity in product requirements
• failure and recovery in AI workflows
• maintaining consistent behaviour in real-world usage

Tech Environment

• Next.js / React
• Python and Node.js
• SQL / NoSQL databases
• LLM ecosystem (OpenAI, Anthropic, etc.)
• Docker and Kubernetes
• AWS, Azure or GCP is fine

The emphasis is on how you design and ship product features, rather than any single technology.

Team & Working Style

• Remote-first
• Small, highly capable engineering team
• Strong emphasis on ownership and delivery
• Comfortable working across frontend, backend and evolving systems 

Senior Python Backend Engineer (AI / LLMs)
Remote (United Kingdom) | Permanent | Flexible compensation + equity

We’re working with a well-established, tech-led business that is building a new AI product focused on handling real-world tasks, workflows and decision-making.

This is a small, high-calibre team building systems where AI capability is translated into reliable, structured product behaviour, with a strong emphasis on execution and consistency in real usage.

The product focuses on:

• Long-running workflows

• Persistent context

• Multi-step task execution

• Integration with external systems

The core challenge is designing systems that can apply model capability in a structured and reliable way, even when behaviour is non-deterministic.

The Role
This role sits in the backend layer that connects models to the product.

The focus is on designing and operating systems that enable:

• AI workflows to run end-to-end

• APIs to serve AI features consistently

• Systems to perform reliably under real usage conditions

You’ll be working on:

• Inference pipelines and orchestration layers

• Backend services for AI-driven workflows

• Performance considerations such as latency, throughput, batching and caching

• Monitoring, logging and production reliability

What They Care About
The hiring bar is centred around real production experience:

• Whether you have owned backend systems end-to-end in production

• Your experience working with AI systems beyond simple integration

• Your exposure to practical challenges such as:

• latency and throughput optimisation

• monitoring and observability

• debugging and incident handling

• scaling systems in production environments

Tech Environment

• Python (primary backend language)

• Node.js

• SQL / NoSQL databases

• LLM ecosystem (OpenAI, Anthropic, etc.)

• Docker and Kubernetes

• AWS, Azure or GCP is fine

The emphasis is on how you design and run systems, rather than the specific cloud used.

Team & Working Style

• Fully remote-first, work from anywhere

• Small, highly capable engineering team

• Strong emphasis on ownership and delivery

• Comfortable working in evolving systems and making pragmatic decisions.