Product Architect (AI) Porto

Product Architect (AI)
Porto
Porto, Porto District, Portugal

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 Test
Rail or similar software testing tools is a plus. Responsibilities:Develop a deep understanding of both the product code base and AI tools, including LLM applications, through
- 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
General requirements:Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field4+ 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), Llama
Index, and Lang
Chain
Experience with cloud platforms (AWS)Experience with
- tuning LLMs. Proficiency in Ruby (must-have), Type
Script (nice to have) and Python (nice to have)Familiarity with MLOps best practices, including:Model deployment, monitoring, and versioning. Model lifecycle management. 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
Using embeddings for LLM-based applications. System Design & Best Practices:Experience designing scalable and
- effective AI architectures that handle
- 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. Collaboration & Documentation:Ability to create documentation that communicates:Business-level understanding of AI solutions. Technical implementation details for developers and engineers. Ability to work with
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