We are looking for an AI Product Engineer who can help our development team accelerate productivity, automate engineering workflows, and integrate AI features into our product. This role is hands-on and focused on practical AI implementation, not academic research. You will work closely with the software development team to build internal AI tools, automate repetitive tasks, improve code quality, and deliver AI-powered product features.

1. Developer Productivity & Internal Tools • Build internal AI tools (documentation generator, test generator, code review assistant). • Implement automated code quality checks using LLMs. • Create an AI onboarding assistant for new developers to understand architecture. • Build internal chatbots to answer developer questions about codebase and logic. 2. QA & Testing Automation • Develop AI-driven tools that auto-generate unit tests and integration tests. • Implement AI systems for bug root-cause analysis and reproduction steps. • Automate regression testing and increase QA coverage. 3. Product AI Integrations • Integrate LLMs and machine-learning features into customer-facing products. • Build APIs for summarization, recommendations, search, personalization, etc. • Implement vector databases (Pinecone, Weaviate, Chroma) where needed. • Deploy and monitor AI features in production. 4. Business & Workflow Automation • Create AI workflows for internal business operations: o Email summarization o Invoice automation o Report generation o HR/Scrum meeting summary tools 5. Architecture & Technical Leadership • Evaluate AI technologies and propose implementation strategies. • Build small prototypes rapidly and iterate with stakeholders. • Work with backend engineers to ensure scalability and security. • Maintain quality, observability, logging, and performance of AI systems.

Technical Skills • Strong software engineering background (Python, Node, or Java). • Experience integrating LLM APIs (OpenAI, Anthropic Claude, Azure AI). • Knowledge of embeddings, vector databases, and retrieval-augmented generation (RAG). • Familiarity with ML frameworks (PyTorch/TensorFlow preferred but not mandatory). • Experience with Docker, cloud (AWS, Azure, GCP). • Git, CI/CD pipelines, testing frameworks. AI Skills • Prompt engineering and LLM-tooling design. • Ability to fine-tune or adapt models on company data. • Building prototypes and rapidly iterating. • Understanding AI safety, bias, and evaluation practices. Soft Skills • Strong communication and ability to collaborate with existing developers. • Ability to translate business needs into technical AI solutions. • Ownership mentality with rapid execution.