Software Engineer, AGI Platform

JazzX AI

• $130K — $160K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 3+ years of experience in production software systems design and development.
  • Proficient in Java, Python, Go, JavaScript/TypeScript, or C++.
  • Experience with backend services, APIs, and distributed systems.
  • Familiarity with cloud platforms, containers, and deployment practices.
  • Strong grasp of software quality, debugging, and reliability principles.
  • Adept at navigating fast-paced, cross-functional environments.

Responsibilities

  • Design and develop scalable backend services and APIs for AI applications.
  • Build reliable systems for workflow execution and orchestration.
  • Contribute to cloud-native infrastructure focused on performance and reliability.
  • Integrate LLMs and related AI services into product experiences.
  • Operationalize AI systems with effective monitoring and governance.
  • Collaborate with research engineers to implement experimental capabilities.
  • Drive engineering excellence through clean coding and best practices.

Benefits

  • Collaborative and cross-disciplinary work environment.
  • Opportunity to work on cutting-edge AI technologies.
  • Exposure to various AI systems and architectures.
  • Focus on software quality and operational excellence.
  • Access to continuous learning and development opportunities.
Full Job Description
Role Overview

As a Software Engineer, AI Platform, you will help design, build, and operate core services and product capabilities for JazzX AI's enterprise platform. This is a software engineering role with strong exposure to modern AI systems, including LLM-powered services, agentic workflows, retrieval systems, evaluation pipelines, and production-grade AI infrastructure.

You will work across backend services, APIs, platform components, and product workflows that enable AI-driven enterprise applications to run reliably in production. You will collaborate closely with research engineers, product managers, designers, and other software engineers to turn AI concepts into scalable, secure, and operable systems.
What You Will Do
Build Core Platform Services
  • Design and develop scalable backend services, APIs, and platform components for enterprise AI applications.
  • Build reliable systems for workflow execution, orchestration, observability, and evaluation.
  • Contribute to cloud-native infrastructure and service architecture with strong focus on performance, reliability, and maintainability.
Develop AI-Enabled Product Capabilities
  • Integrate LLMs, retrieval pipelines, agent frameworks, and related AI services into production-grade product experiences.
  • Build and improve services that support prompt execution, tool use, knowledge retrieval, memory, and evaluation workflows.
  • Help operationalize AI systems with proper monitoring, traceability, governance, and scalability.
Collaborate Across Disciplines
  • Work closely with research and AI engineers to productionize experimental capabilities.
  • Partner with product, design, and architecture teams to translate complex requirements into modular and scalable implementations.
  • Contribute to technical design discussions and help shape platform direction.
Drive Engineering Excellence
  • Write clean, testable, maintainable code.
  • Improve system operability through strong testing, logging, alerting, and automation.
  • Contribute to code reviews, technical standards, and engineering best practices.
What We're Looking For
Required
  • 3+ years of experience designing and building production software systems.
  • Strong software engineering fundamentals in one or more of: Java, Python, Go, JavaScript/TypeScript, or C++.
  • Experience building backend services, APIs, and distributed systems.
  • Experience with cloud platforms, containers, and modern deployment practices.
  • Strong understanding of software quality, testing, debugging, and operational reliability.
  • Ability to work in a fast-moving, cross-functional environment and turn ambiguous requirements into working systems.
Good To Have:
  • Experience with AI/ML-enabled products or platforms.
  • Familiarity with LLMs, retrieval-augmented generation, prompt workflows, or agent frameworks such as LangChain, LlamaIndex, CrewAI, or AutoGen.
  • Exposure to MLOps, observability, evaluation frameworks, or experimentation platforms.
  • Experience with knowledge graphs, ontology-driven systems, or enterprise search and retrieval is a plus.
  • Experience with Kubernetes, Docker, infrastructure as code, and cloud-native architecture.

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