AI Software Engineer

Arva is a machine learning software-based SaaS company developing technology for regenerative agriculture and sustainability. The AI Software Engineer will design, build, and operate AI-enabled services, including agentic systems, AI/ML services, backend APIs, data pipelines, integrations, and user-facing tools. The role also involves deploying services on AWS, implementing QA/QC processes, and collaborating with data scientists, agronomists, and product stakeholders.

Responsibilities:

• Design, build, and maintain LLM-powered and agentic workflows using tools like LangGraph and LangChain, AWS Bedrock AgentCore, including tool use, retrieval, memory, orchestration, and multi-step reasoning pipelines
• Develop and productionize AI/ML services in Python, including work with Mixture-of-Experts (MoE) and other multi-model architectures for routing, specialization, and ensemble prediction
• Build and extend backend services and APIs in Django, with PostgreSQL (and PostGIS) as the primary data layer
• Implement front-end features and internal tooling in TypeScript and Vue.js to expose AI capabilities and analytics to users
• Build and optimize data processing pipelines that ingest, clean, normalize, and enrich large volumes of geospatial, agronomic, imagery, and third-party data
• Develop and maintain integrations with external partner and customer APIs, including authentication, rate limiting, schema mapping, error handling, and monitoring
• Deploy, scale, and operate services on AWS using managed compute, storage, database, queueing, and serverless offerings
• Design and execute QA/QC processes for both software and data — automated testing, validation rules, anomaly detection, model evaluation, and regression checks — so that model outputs meet the accuracy and auditability standards our customers and verification bodies require
• Participate in code review, architecture discussions, and technical planning; write clear documentation for the systems you build
• Partner with data scientists, agronomists, and product stakeholders to translate scientific and business requirements into production systems

Requirements:

• 2–5 years of professional software development experience building and shipping production systems
• Strong proficiency in Python, including experience with modern application frameworks, testing, and packaging practices
• Demonstrated experience building applications or services that use large language models or other machine learning models in production
• Working knowledge of relational databases and SQL, with practical experience designing schemas and writing performant queries
• Experience developing and consuming RESTful APIs
• Familiarity with cloud infrastructure (AWS preferred) and standard software delivery practices including version control, CI/CD, and automated testing
• Ability to work independently on ambiguous problems, and to communicate technical trade-offs clearly to both technical and non-technical colleagues
• Bachelor's degree in Computer Science, Software Engineering, a related technical field, or equivalent practical experience
• Only applicants currently eligible to work in the United States will be considered for this position
• Building agentic and LLM applications with tools like Amazon Bedrock, LangGraph and LangChain
• Familiarity with Mixture-of-Experts (MoE) architectures, model routing, and ensemble or multi-model system design
• Production experience with Python and the Django framework
• Proficiency with TypeScript, JavaScript, and Vue.js
• Advanced PostgreSQL experience, including performance tuning and the PostGIS extension
• Experience with geospatial data formats, coordinate systems, raster and vector processing, satellite or remote sensing imagery, and libraries such as GDAL, rasterio, GeoPandas, or Shapely
• Building large-scale ETL/ELT pipelines, batch and streaming workflows, and orchestration tooling
• Depth across services such as Lambda, S3, ECS/Fargate, RDS, Batch, SQS/SNS, Step Functions, and IAM
• Designing and maintaining robust third-party and partner API integrations
• Establishing automated quality assurance and quality control processes for software, data, and model outputs
• Prior work in agriculture, agronomy, remote sensing, climate, energy, or environmental markets