AI Software Engineer — Production LLM Systems & Pipelines

Arva Intelligence

Houston (TX)

On-site

USD 115,000 - 145,000

Full time

14 days+
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Benefits offered by this job

Target annual bonus
401(k) contribution
Flexible PTO
Stock options
100% employer-paid health insurance

Job summary

Arva Intelligence in Houston, TX is seeking an AI Software Engineer to design and operate AI-enabled services at the core of the Arva platform. You will build agent-based systems using LangGraph/LangChain and develop durable APIs, pipelines, and data infrastructure to scale production-grade AI.

This hands-on role requires shipping production code, collaborating with data scientists and product teams, and applying modern AI techniques to agritech challenges.

Qualifications

  • 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.

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.

Skills

Python
LLMs in prod
SQL
REST APIs
AWS
CI/CD
Independent work

Education

Bachelor's degree

Tools

Django
PostgreSQL
PostGIS
TypeScript
Vue.js
LangGraph
LangChain
AWS Bedrock

Job description

Arva Intelligence in Houston, TX is seeking an AI Software Engineer to design and operate AI-enabled services at the core of the Arva platform. You will build agent-based systems using LangGraph/LangChain and develop durable APIs, pipelines, and data infrastructure to scale production-grade AI.

This hands-on role requires shipping production code, collaborating with data scientists and product teams, and applying modern AI techniques to agritech challenges.

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