Senior Full Stack Developer - System Design & Architecture

Overview

Work Experience : 4 to 6 years

We are seeking a Senior Full Stack Developer with strong expertise in domain modeling and backend systems development, who is comfortable navigating the entire technology stack-from modern web UI frameworks to scalable data repository design for sophisticated cloud applications.

This role will play a key part in the development of our global field and crop management system, as well as the design, enhancement, and support of systems that enable research, plant breeding, quality assurance, and product supply chain operations.

The ideal candidate is also enthusiastic about AI-enabled software development, applying modern tooling and techniques to improve developer productivity, system intelligence, and overall solution quality.

Key Responsibility

System Architecture & Design

• Design and evolve robust domain models reflecting real-world agricultural, research, and supply chain processes
• Contribute to backend architecture decisions with a focus on scalability, maintainability, and performance
• Collaborate with architects and stakeholders to translate complex domain requirements into clean, extensible systems
  
  

• Develop and maintain backend services (APIs, services, workflows) using modern cloud-native patterns
• Design and optimize data persistence layers, including relational and non-relational data stores
• Ensure data integrity, traceability, and auditability across research and operational workflows
  
  

• Work across the stack as needed, including frontend frameworks, API integration, and both UI and server-side/cloud-side performance tuning
• Partner effectively with frontend developers by defining clean contracts, APIs, and data schemas
• Support end-to-end feature delivery from UX considerations through backend implementation
  
  

• Leverage AI-assisted coding tools for faster development, refactoring, testing, and documentation with components
• Explore opportunities to embed AI/ML capabilities into applications (e.g., analytics, recommendations, automation)
• Promote best practices in responsible, maintainable use of AI within software engineering & Quality :
• Work closely with product managers, scientists, field teams, and business stakeholders
• Participate in code reviews, architectural discussions, and technical mentoring
• Champion engineering excellence, automated testing, and continuous improvement
  
  

• Design and implement multi agent workflows (planning, delegation, tool use, and retries) with frameworks such as LangGraph/LangChain, CrewAI, Swarm, AutoGen, Semantic Kernel, or Google ADK-choose the right orchestration pattern for the job (deterministic graphs, role based teams, conversation driven, or function centric).
• Build and maintain MCP servers and clients to expose internal tools (search, RAG, code exec, retrieval, databases, SaaS APIs) with secure authentication, schema first contracts, and versioning.
• Implement memory strategies (ephemeral session memory vs. persistent profile/state), tool routing, and guardrails (input/output validation, prompt injection defenses, PII handling).
• Add offline/online evaluations (unit tests for agents, golden test sets, regression suites) and observability (traces, cost tracking, latency/error budgets).
  
  

• 4 to 6 years of professional software development experience
• Strong experience with backend systems development and domain-driven design (DDD) concepts
• Proficiency in at least one modern backend language/framework (e.g., Java, C#, Python, Node.js)
• Solid understanding of cloud platforms (AWS, Azure, or GCP) and cloud-native architectures
• Experience designing and working with complex data models and repositories
• Working knowledge of modern frontend frameworks (React, Angular, Vue, etc.)
• Experience with APIs, messaging, and asynchronous processing
• Strong communication skills and ability to work across disciplines
  
  

• Experience in agriculture, life sciences, research systems, or supply chain platforms
• Familiarity with geospatial, time-series, or experimental data
• Experience supporting global, multi-region applications
• Exposure to MLOps, AI/ML integration, or data science workflows
  
  

(ref:hirist.tech)