Job description

GT was founded in 2019 by a former Apple, Nest, and Google executive. GT’s mission is to connect the world’s best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands.

About the Role:

We are looking for an experienced ML Architect (with Data Experience) to design and improve AI/ML-driven solutions and data architectures. In this role, you will lead the discovery phase of projects, working closely with stakeholders to gather requirements, assess feasibility, and define high-level solutions.
Expected Involvement: +- 20 hrs/week.

Responsibilities:

Review the architecture of an existing product & plan potential improvements
Transform business features into technical requirements
SOW estimation and planning
Provide data-driven insights and visualizations of existing usage patterns
Design and maintain the foundational architecture of the Product Ecosystem, ensuring scalability and performance
Develop architectural blueprints and technical designs that adhere to best practices and industry standards
Define and enforce coding standards, code reviews, and quality assurance processes
Conduct architecture reviews to identify potential risks and mitigate technical debt

Essential knowledge, skills & experience:

5+ years of experience in data architecture, AI/ML solutions, or cloud-based data engineering
Experience in data solution architecture and AI/ML integration in data platforms
Proven experience in designing and implementing large-scale data engineering solutions in cloud environments (AWS, Azure, or GCP)
Experience with Apache Spark, Databricks, or Azure solutions (or similar cloud-based data processing technologies)
Experience with ML and Deep Learning frameworks (e.g., PyTorch, Hugging Face, TensorFlow)
Experience with data warehousing, ETL processes, and real-time data streaming, with a focus on core data principles rather than specific database expertise
Familiarity with open-source technologies and tools in data engineering
Knowledge of architectural patterns and practices
Understanding of different types of ML model usage

Nice-to-have:

Relevant certifications (e.g.,AWS, GCP, or Azure)
Familiarity with specific database technologies such as Snowflake, Redshift, DuckDB, MongoDB/Atlas, Hive, etc.
Experience in discovery phases (gathering requirements, planning, and assessing feasibility)
Knowledge of advanced analytics tools (Python)
Knowledge of Transformers architecture