About the role

Applied AI Engineer developing scalable AI-powered applications for Pulsora's sustainability platform. Building and deploying sophisticated AI-driven products using leading LLM APIs.

Responsibilities

Build & Ship AI-Powered Products
Design, develop, and deploy LLM-powered applications and agent-based systems in production
Own the full product lifecycle from concept → production → iteration across backend and frontend
Build scalable, maintainable systems that solve real customer problems
Develop prompt strategies, context management (RAG, memory), and agent workflows
Integrate and optimize across leading LLM APIs (OpenAI, Anthropic, Gemini, open-source)
Continuously improve system quality, reliability, and performance
Use AI-assisted coding tools (e.g., Cursor, Copilot, Claude Code) to accelerate development
Validate, test, and safely integrate AI-generated code into production systems
Own testing strategy including unit, integration, and end-to-end testing
Define and implement evaluation frameworks for AI outputs (quality, consistency, failure modes)
Debug production issues and optimize for latency, cost, and scalability
Translate ambiguous requirements into working solutions
Partner with product, design, and customer-facing teams to deliver impactful features
Collaborate effectively with distributed and offshore engineering teams

Bachelor’s degree in Computer Science, Engineering, or related field preferred (or equivalent hands on experience)
1-2 years of experience building AI-powered applications (AI agents or AI-assisted coding) and building and deploying LLM-powered applications or agents in production
Experience integrating major LLM APIs (e.g., OpenAI, Anthropic, Gemini, open-source)
Working knowledge of prompt design, context management (RAG, memory, tool use), and multi-step/agentic workflows
Ability to take products from concept to production and iteration across backend and frontend systems, including working across the full engineering lifecycle
Practical experience using tools like Claude Code, Cursor, OpenAI, Microsoft Copilot, including the ability to safely integrate AI-generated code into production (validation, CI/CD)
Experience debugging production issues, optimizing performance (latency, cost), and building maintainable systems
Ability to define evaluation strategies for AI outputs (quality, consistency, failure modes) and experience with AI-driven testing and reliability practices.