Applied AI Engineer

Responsibilities

• Design and optimize LLM-based systems for high-quality, context-rich code reviews
• Build and refine agentic workflows that reason across multiple steps and contexts
• Develop and maintain knowledge base and retrieval pipelines (e.g., chunking, embeddings, semantic search)
• Deploy generative AI models and pipelines into production and monitor performance
• Collaborate across teams to ensure that AI outputs align with user needs and product goals
• Analyze human-in-the-loop feedback and usage data to iteratively improve system performance
• Apply RLHF, ranking, and reward modeling techniques to improve response quality over time
• Stay current with the latest generative AI developments and apply them to new use cases

Qualifications

• Education: Degree in Computer Science, Engineering, Artificial Intelligence, or related field, or equivalent practical experience
• Experience: 3+ years applying ML or LLM-based systems in real-world production environments, with at least 2 years of industry experience focused on generative AI
• Technical Skills: Strong programming skills in TypeScript and Python
• AI Frameworks: Experience with tooling such as LangChain, LlamaIndex, OpenAI APIs, or vector databases like Pinecone or Lancedb
• Prompt Engineering: Strong skills in prompt engineering
• Data Fluency: Ability to extract insight from telemetry, logs, user signals, and structured feedback
• Practical Mindset: Comfortable applying research-inspired methods to solve concrete product challenges
• Cross-Functional Collaboration: Experience working across product, engineering, and design to deliver production-grade systems

Bonus Points

• Experience optimizing RAG systems and tuning retrieval performance using custom embeddings or search strategies
• Hands-on experience with RLHF pipelines, reward modeling, or behavioral policy tuning in LLMs
• Experience integrating LLM systems into developer tooling or collaborative workflows
• Track record of contributions to open-source projects or publications in applied AI/ML