Applied AI Engineer

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.

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