Applied AI Engineer

Cherry; Greater London; On-site; GBP 110,000 - 150,000; Full time

Benefits offered by this job

Equity

Learning & Development budget

Office in central London

Job summary

Cherry Ventures on behalf of a high-growth fintech portfolio company is hiring an Applied AI Engineer to build AI agents and production-ready capabilities transforming how finance teams work.

This is a product engineering role with ownership of agent-based features end to end—from architecture and evaluation through backend integration, deployment, and operation in production.

Qualifications

• Seasoned software engineer with experience building and operating production applications.
• Proficient in Python and/or Java, across backend services, APIs, data and architecture.
• Experience shipping at least one agent or LLM-powered product end to end.
• Experience integrating agents into a broader product and backend architecture, not just prototypes.
• Experience evaluating agents or other non-deterministic AI systems.

Responsibilities

• Build and ship AI agents that automate complex finance workflows.
• Own features from concept and prototype through production deployment.
• Integrate agents with backend services, APIs, data model, permissions and workflows.
• Design prompts, tools, memory, workflows and orchestration strategies for production agents.
• Collaborate with data scientists to integrate ML models into production systems.

Skills

Python

Java

Agent orchestration

Context engineering

AI evaluation

Production engineering

Tools

REST APIs

Kafka

Vector search

Job description

Cherry Ventures is hiring on behalf of a high-growth Series C fintech portfolio company.

About the Role

We're hiring an Applied AI Engineer to build AI agents and intelligent product capabilities that transform how finance teams work.

This is a product engineering role, not an isolated prototyping or research role. You'll own agent-based features end to end: from architecture and evaluation through backend integration, deployment, and operation in production.

What You'll Do

Build and ship AI agents

• Design and build agents that automate complex finance workflows.
• Own features from initial concept and prototype through production deployment.
• Integrate agents deeply with backend services, APIs, the data model, permissions, and product workflows.
• Design how agents securely access and use data across the platform.
• Turn emerging AI capabilities into reliable, customer-facing product features.

Evaluate and improve agent performance

• Build systematic evaluations for agent quality, reliability, and business impact.
• Create representative test datasets, evaluation criteria, regression tests, and human-review processes.
• Measure and improve accuracy, latency, cost, and user experience.
• Establish observability and feedback loops that make agent behavior understandable and continuously improvable.

Develop the AI application architecture

• Apply context-engineering techniques such as RAG, MCP, and knowledge graphs.
• Design prompts, tools, memory, workflows, and orchestration strategies for production agents.
• Select and use appropriate orchestration frameworks and comparable technologies.
• Build appropriate guardrails, approval steps, and human-in-the-loop controls for sensitive financial workflows.

Integrate machine-learning capabilities

• Collaborate with data scientists to integrate ML models into production systems.
• Build the services, data flows, APIs, and operational tooling required to make models usable within the product.
• Take responsibility for production integration rather than handing prototypes to another team.

About You

• A seasoned software engineer with experience building and operating production applications.
• Highly proficient in Python and/or Java, and comfortable across backend services, APIs, data, and application architecture.
• You've built and shipped at least one agent or LLM-powered product capability end to end.
• You've personally integrated agents into a broader product and backend architecture, not just developed standalone prototypes.
• You have practical experience evaluating agents or other non-deterministic AI systems.
• You understand how to provide agents with the right data and context while respecting security, permissions, and privacy.
• You have hands-on experience with prompt engineering, context engineering, and agent orchestration.
• You balance rapid experimentation with reliable, maintainable production engineering.
• You communicate clearly and collaborate effectively across engineering, product, and data science.

You don't need experience with every technology below. Strong engineering judgment and demonstrated end-to-end ownership matter most.

• Agent orchestration: modern agent and LLM orchestration frameworks
• Context engineering: RAG, MCP, knowledge graphs, tool use, memory, and retrieval systems
• AI evaluation: offline and online evaluations, test datasets, regression testing, observability, and human review
• Language models: Gemini, OpenAI, Anthropic, Llama, Mistral, or similar
• Backend engineering: Python or Java, REST APIs, Kafka, microservices, and distributed systems
• Data systems: SQL, PostgreSQL, BigQuery, vector search, and data pipelines
• Cloud AI platforms: GCP and Vertex AI, or comparable platforms

What's on Offer

• Top-of-market compensation package, including equity.
• Vibrant offices at the heart of a collaborative culture, with in-person time across weekly breakfasts and Friday demos.
• 20 days "work from abroad", a Learning & Development budget, and other local benefits.