Product Architect (AI) - Remote

Overview

The Product Architect owns all technical matters related to their assigned products, with a focus on creating AI-powered tools that enhance existing product capabilities. This includes designing and orchestrating feature implementations for AI-driven functionalities; maintaining, guiding, and improving the architectural integrity of these AI enhancements; and ensuring best practices and architectural guidelines are followed across the AI and machine learning domains. Familiarity with TestRail or similar software testing tools is a plus.

In Short

• Develop a deep understanding of both the product code base and AI tools, including LLM applications, through hands-on experimentation and exploration.
• Design and implement AI-powered tools that integrate seamlessly with existing product workflows.
• Assess and shepherd the technical delivery of AI enhancements to ensure adherence with best practices.
• Drive continual improvement of development processes, with an emphasis on machine learning pipelines and LLM training.
• Collaborate with the Product Management Team and other stakeholders, contributing to the product roadmap, product requirements, and product release schedule.
• Identify gaps in the technical aspects of the end user experience.
• Estimate software development requirements and effort, propose alternatives, and prioritize development tasks for a given area.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 4+ years of experience in software engineering.
• Hands-on experience in building and deploying AI/ML solutions in production.
• Strong experience in Natural Language Processing (NLP) with LLMs.
• Strong understanding of API development for integrating AI solutions.
• Experience with Retrieval-Augmented Generation (RAG), LlamaIndex, and LangChain.
• Experience with cloud platforms (AWS).
• Experience with fine-tuning LLMs.
• Proficiency in Ruby (must-have), TypeScript (nice to have) and Python (nice to have).
• Familiarity with MLOps best practices, including model deployment, monitoring, and versioning.
• Ability to build scalable and efficient AI pipelines.
• Understanding of data engineering best practices (ETL pipelines, data lakes).
• Knowledge of data processing techniques, including preprocessing text and unstructured data for NLP applications.

Benefits

• Experience designing scalable and cost-effective AI architectures that handle real-world constraints.
• Strong knowledge of performance optimization for AI-driven applications.
• Understanding of security and compliance in AI solutions.
• Experience working with containerization (Docker, Kubernetes) for AI model deployment.
• Ability to create documentation that communicates business-level understanding of AI solutions.
• Ability to work with cross-functional teams including data engineers, software engineers, and product managers.