Senior AI Product Engineer

Overview

Key Responsibilities

• **AI Feature Delivery & Integration**
• Lead the end-to-end development of AI-powered features, from initial prototyping to production deployment.
• Integrate LLMs and ML models into existing web/mobile applications and backend APIs.
• Design "AI-first" user journeys, ensuring features like autocomplete, summarization, and intelligent search are intuitive and high-performing.

2. Prompt Engineering & Agentic Workflows

• Craft, test, and optimize complex **system prompts** to ensure high-quality, safe, and consistent model outputs.
• Build and manage **Agentic workflows** (using frameworks like LangChain, CrewAI, or AutoGen) to automate multi-step business processes.
• Implement robust error-handling and "fallback" logic for non-deterministic AI responses to maintain a seamless UX.

3. Squad Collaboration (The "Product" Side)

• Act as the AI subject matter expert within your **Product Squad**, helping PMs understand what is technically feasible vs. what is "AI hype."
• Work closely with UX/UI designers to handle AI-specific design challenges (e.g., streaming text latency, "thinking" states, and user feedback loops).
• Participate in agile ceremonies, contributing to grooming, planning, and rapid iteration.

4. Hybrid Implementation

• Develop applications that can switch seamlessly between **Cloud APIs** (for speed/innovation) and **On-Premises models** (for data privacy and cost-efficiency).
• Ensure that AI features are performant across different hosting environments without sacrificing user experience.
• **8+ years** in Full-Stack or Backend Software Engineering.
• **3+ years** of experience specifically building and shipping AI-integrated features.
• Proven ability to work in an **Agile/Scrum** environment within a "Squad" structure.
• Mastery of **Python** and/or **TypeScript/JavaScript** (React/Node.js).
• Hands-on experience with **LangChain, LlamaIndex, or Haystack**.
• Expertise in RESTful APIs, GraphQL, and working with Vector DBs (Pinecone, Weaviate).
• Familiarity with Docker, Kubernetes, and deploying to AWS/Azure or local GPU environments.