Senior Software Engineer, Coalition

• Core Development: Expert-level API and service development in Python (required) and/or Go. Experience with SQL, database management
• AI Architecture: Hands-on experience building RAG pipelines, managing vector databases, and orchestrating multi-agent systems
• Cross-Functional Fluency: Proven ability to communicate technical requirements effectively across Data Science, Data Engineering, and Product domains
• Cloud Native Principles: Deep understanding of production-tested principles including horizontal scaling, 12-factor application design, and security principles like OWASP
• Observability: Experience monitoring AI performance and analyzing model/software errors using tools like Sentry, DataDog, or Jaeger
• DevOps Tools: Proficiency in container-based development with Docker and source control via Git
• Experience: At least 5+ years of hands-on software development experience—"It’s not the years, it’s the miles."

Desirable

• We are hiring a Senior Software Engineer to lead the development of our internal AI platform and orchestration components, helping us accelerate the deployment of intelligent Agents and RAG pipelines across the organization
• You will own the core infrastructure and SDKs that bridge the gap between raw LLM capabilities and production-grade applications, turning complex AI research and data into seamless, scalable tools for our engineering teams
• Your work will directly enable us to automate sophisticated workflows and deliver smarter, AI-driven products to our users
• You’ll be a key architect and builder of our internal AI platform, designing components that power various AI Agents and Retrieval-Augmented Generation (RAG) pipelines
• You prioritize ensuring that complex logic branches and AI workflows are well-understood, tested, and focused on engineering excellence
• You will build APIs and services that are highly intuitive to consume, ensuring our internal teams can seamlessly integrate AI capabilities into their workflows
• AI Platform Engineering: Build and maintain the core infrastructure, services, and SDKs that empower the rest of the company to leverage LLMs and Agentic workflows
• Scientific Collaboration: Partner closely with Applied Scientists and Data Scientists to translate experimental models and research into scalable, production-grade AI services
• Data Strategy: Work with Data Engineers to design robust data pipelines that feed into RAG systems, ensuring high-quality, real-time data retrieval for AI context
• Internal Onboarding: Act as a technical consultant and facilitator for other engineering teams, helping them successfully onboard their specific AI use cases onto the platform
• Design & Document: Design, document, and deploy public AI interfaces that provide an exceptional developer experience for internal users
• Operational Excellence: Collaborate with Site Reliability Engineers (SRE) to deliver AI applications in a repeatable, stable, and highly efficient software development lifecycle
• Quality Engineering: Develop comprehensive test cases for AI models and workflows, working with QE to ensure deterministic and reliable outcomes in a non-deterministic AI landscape