Mid Software Engineer

Yagro

12h•Hybrid

About The Position

YAGRO Ltd is seeking a Mid Software Engineer with strong Python and TypeScript skills and practical experience integrating AI into production applications. The role is responsible for building and maintaining enterprise Software as a Service (SaaS) and white label technology solutions for agri-food enterprises. Working alongside senior engineers, you will take ownership of development tasks, contribute across the stack and help deliver reliable, user-friendly software, applying AI where it adds value. You will bring two to five years of professional software development experience and a relevant degree or a proven track record of delivering production-quality software. Whilst this is a largely remote role, you will be required to attend the Cambridge office one day a week.

Requirements

Strong Python and TypeScript skills
Practical experience integrating AI into production applications
Two to five years of professional software development experience
Relevant degree or a proven track record of delivering production-quality software

Nice To Haves

Experience with Vue 3 and Nuxt in SPA mode, or equivalent reactive frameworks
Experience with asynchronous Python services using FastAPI or equivalent technologies
Experience with relational databases, preferably PostgreSQL, and SQL
Experience with multi-stage asynchronous processing pipelines
Experience integrating LLM or ML model APIs
Experience with Agile/Scrum or equivalent methods
Experience with collaboration tools such as GitHub, GitLab and Jira
Experience with automated testing frameworks such as PyTest, Jest or Playwright
Experience with AWS services (Lambda, RDS, S3, Lightsail)
Experience with Terraform
Experience with CI/CD workflows using GitHub Actions
Experience with SQL-first database migrations using tools such as dbmate
Experience with event-driven processing
Experience with containerised Lambda functions
Experience with presigned URL uploads
Experience with asynchronous validation
Experience with vector search, embeddings, RAG, fine-tuning, prompt engineering or AI orchestration frameworks such as LangChain
Experience with custom agent and pipeline tooling
Experience with data-processing libraries such as Polars or pandas
Experience with columnar or Parquet-based storage

Responsibilities

Build and maintain production-quality software using Python and TypeScript, applying sound engineering principles, design patterns and modular code structure.
Develop applications using Vue 3 and Nuxt in SPA mode, or equivalent reactive frameworks, alongside asynchronous Python services using FastAPI or equivalent technologies.
Work confidently with relational databases, preferably PostgreSQL, and SQL.
Break down technical problems, diagnose and resolve bugs, and contribute scalable, maintainable solutions with guidance from senior engineers where needed.
Understand multi-stage asynchronous processing pipelines and take ownership of individual stages, including uploading, validating, processing and storing data.
Integrate LLM or ML model APIs into production features, writing and reviewing prompts with appropriate safeguards against prompt injection and invalid outputs.
Take ownership of assigned work from development through deployment, showing initiative and communicating progress, dependencies and challenges early.
Contribute to planning and delivery using Agile/Scrum or equivalent methods and collaboration tools such as GitHub, GitLab and Jira.
Work effectively with engineering, Product, QA and design colleagues, participating in code reviews and providing constructive feedback.
Share knowledge, support colleagues and, where appropriate, help onboard new team members.
Work alongside senior engineers, including colleagues experienced in AI and retrieval-augmented generation (RAG), to build the team’s confidence in applying AI to practical problems.
Contribute to frontend and backend work according to your experience, developing a broader understanding of APIs, distributed systems and microservices.
Maintain clear code, system and process documentation, and develop reliable automated tests using frameworks such as PyTest, Jest or Playwright.
Follow and promote good engineering practices, addressing security risks, performance bottlenecks and reliability issues within the systems you work on.
Build and maintain services in AWS, using Lambda, RDS, S3 or Lightsail as appropriate to the product.
Apply your understanding of AI-related cost, latency and reliability to implementation decisions, including recognising when a deterministic approach is more suitable.
Where you have relevant experience, contribute to infrastructure managed through Terraform, CI/CD workflows using GitHub Actions, and SQL-first database migrations using tools such as dbmate.
Support event-driven processing, containerised Lambda functions, presigned URL uploads and asynchronous validation where these patterns are used.
Develop your technical skills through feedback, practical delivery and knowledge sharing, taking an active interest in developments across software engineering and AI.
Build your understanding of the sectors YAGRO serves, the problems customers face and how users interact with our products.
Explore relevant approaches with senior engineers, drawing on any experience of vector search, embeddings, RAG, fine-tuning, prompt engineering or AI orchestration frameworks such as LangChain.
Help evaluate model output quality through structured examples and lightweight evaluation sets.
Where relevant, develop your knowledge of custom agent and pipeline tooling, data-processing libraries such as Polars or pandas, and columnar or Parquet-based storage, assessing their usefulness against the problem being solved.
Align your work with YAGRO’s business objectives and customer needs, contributing reliable, user-friendly solutions that deliver practical value.
Build approachable working relationships across the business and communicate technical information clearly, both verbally and in writing.
Assist colleagues when they bring customer feedback or issues to engineering, asking questions to understand the underlying need.
Identify opportunities where AI could improve a product or workflow, then work with Product, Business Development and engineering colleagues to assess feasibility, benefits and trade-offs before recommending an approach.