As a Senior Software Engineer, you will play a key individual contributor role in designing, developing, and maintaining production-ready tools and products for the Brilliant Harvest platform, with a strong focus on ETL pipelines for data extraction and ingestion. Working closely with our Head of AI, you will help expand and improve our Document Ingestion and RAG-based AI Assistant — from data pipelines to LLM integration. You'll participate in brainstorming, design reviews, code reviews, and architecture evolution discussions, and contribute to the analysis of business requirements, technical design recommendations, and effort estimation.
This role is ideal for someone who thrives in a fast-paced environment, loves solving complex problems, and is excited about taking ideas from concept to production in a domain where AI is transforming a legacy industry.
What You'll Do:
Design, develop, document, and maintain prototypes and products
Design, develop, document, and maintain ETL tools for data extraction and ingestion and products
Work with our AI Architect to expand and improve on our Document Ingestion and RAG based AI Assistant
Participate in brainstorming, design reviews, code reviews and architecture evolution discussions
Contribute to the analysis of business requirements, prepare design and implementation recommendations, and estimate development effort
Review and comment on the technical feasibility of UI/UX designs
Work collaboratively and professionally in cross functional teams to achieve our goals
Contribute to continuous practice improvements
What You Bring:
4+ years of experience as a full stack developer or backend developer with strong ETL skills
Efficient use of LLM tooling
Proficient in C#, with a good knowledge of the .net core ecosystems
Strong Experience with Entity Framework core
Unit and API testing with xUnit, Postman, or others
Working in an agile environment (e.g. Azure DevOps)
Proficient understanding of GIT
Able to write clean, readable, and easily maintainable code
Experience taking an idea from conception to production.
Experience developing applications using an LLM API like GPT
Experience with frameworks like LangChain and VectorDBs
Bonus Points:
Experience with Python
Experience with React Native
Experience with Typescript, Redux Toolkit, CSS, Node.js
Experience with Micro Services Architecture
Application caching with Redis Cache
Docker, Kubernetes
Be part of a high-performing team led by Remi Schmaltz, an entrepreneur with decades of experience launching and growing agriculture businesses.
Remote-first role with a flexible work environment.
A front-row seat to how AI is changing the way equipment dealers, farmers, and contractors work.
A collaborative culture that values growth, learning, and impact.
Competitive compensation, ESOP, and benefits.
Skills Required
- 4+ years of experience as a full-stack or backend developer with strong ETL skills
- Efficient use of LLM tooling
- Proficiency in C# and the .NET Core ecosystem
- Strong experience with Entity Framework Core
- Experience with unit and API testing using xUnit, Postman, or similar tools
- Experience working in an agile environment, such as Azure DevOps
- Proficiency with Git
- Ability to write clean, readable, maintainable code
- Experience taking an idea from conception to production
- Experience developing applications using an LLM API such as GPT
- Experience with frameworks such as LangChain and vector databases
- Experience with Python
- Experience with React Native
- Experience with TypeScript, Redux Toolkit, CSS, or Node.js
- Experience with microservices architecture
- Experience with Redis caching
- Experience with Docker and Kubernetes
What We Do
AltaML is a leading developer of AI-powered solutions. Working with organizations that want to leverage their data using artificial intelligence (AI), AltaML develops solutions that create operational efficiency, reduce risk, and generate new sources of revenue. Through a deep understanding of organizational pain points and challenges, AltaML develops solutions that encompass the entire machine learning (ML) life cycle, from evaluating potential use cases and determining feasibility, to piloting solutions, putting code into production, and ensuring models evolve over time.
