AI Engineer – Fintech

October 26

Apply Now

Receive Emails with Similar Jobs

Tymit

WebsiteLinkedInAll Job Openings

Finance • Fintech

Tymit is a financial services company focused on offering a flexible instalment experience under its client's brand. It provides solutions for businesses to offer card-linked and embedded instalment payment options, helping to boost sales and customer loyalty. With services regulated by the Financial Conduct Authority (FCA) and issued through trusted partners like Transact Payment Limited and Modulr FS Limited, Tymit enables merchants to offer their customers 0% interest instalments over various time frames and for larger purchases up to £12,000. The company's solutions are designed to increase conversions and drive repeat spending without incurring transaction fees, making it an attractive option for businesses looking to provide more than just the standard Buy Now, Pay Later experience.

11 - 50 employees

Founded 2019

💸 Finance

💳 Fintech

💰 Series A on 2022-10

📋 Description

• Ship AI features: Build production-ready LLM applications (RAG pipelines, agents, orchestration layers) that deliver real business impact. • Ingest & normalize: Design and implement data ingestion and transformation pipelines for diverse inputs. • Stand up retrieval: Implement and optimize vector databases (Qdrant, Milvus, Pinecone, pgvector), define chunking/embedding strategies, and improve retrieval quality. • Orchestrate agents: Build and optimize multi-step, agentic workflows using frameworks like LangChain/LangGraph, Agno, or CrewAI. • Tune & optimize: Improve prompts, fine-tune open-source models for specific use cases, and optimize inference stacks for speed, cost, and reliability. • Engineer context: Design and optimise context injection strategies (prompt templates, retrieval augmentation, dynamic context windows, memory management) to improve quality, reliability, safety, and cost-effectiveness of LLM responses. • Observability & evals: Use tools like LangFuse, LangSmith (or similar) to monitor pipelines, run automated evals, and track cost, latency, and quality. • Secure the stack: Defend against prompt injection, data leakage, and unsafe outputs by implementing guardrails, monitoring, and escalation/fallback mechanisms. • Collaborate: Work closely with Data and Product to identify opportunities and deliver high-value AI capabilities. • Learn & adapt: Stay ahead of the curve in LLM orchestration, observability, security practices and emerging open-source frameworks.

🎯 Requirements

• Proven experience shipping LLM-powered systems into production (not just demos). • Strong software engineering background with fluency in Python (APIs, data pipelines, backend services). • Hands-on experience with LangChain, LangGraph, Agno, CrewAI or similar frameworks. • Comfort with vector databases (Qdrant, Milvus, Pinecone, pgvector). • Experience integrating multiple LLM providers (OpenAI, Anthropic, Gemini, AWS Bedrock, and fine-tuned open-source models). • Familiarity with observability tools (LangFuse, LangSmith, or similar) and designing evals for AI systems. • Experience working on AWS cloud infrastructure. • Deep interest in security: Awareness of LLM-specific threats (prompt injection, data exfiltration, malicious tool use) and strategies to mitigate them. • Pragmatic, startup-minded engineer: you deliver working systems quickly and iterate. We love engineers who tinker. If you’ve built your own AI tools, played with agentic frameworks, or contributed to open-source AI projects — share it.

🏖️ Benefits

• 26 days of paid holiday plus bank holidays. • Your birthday off. • Private health insurance. • Budget for home office set up. • Monthly home working allowance. • Flexible working hours. • Referral program. • EUR 1,000 annual learning and development budget. • Length of service recognition program.