About the role

Principal Machine Learning Engineer at Qodea focusing on data quality and ML capabilities. Leading architect role on innovative, data-driven initiatives with a transformative approach.

Responsibilities

Lead the architecture and evolution of scalable, high-performance data pipelines and ML systems, focusing on data ingestion, transformation, quality checks, and enrichment.
Provide technical leadership and mentorship to a cross-functional team of ML Engineers, Data Scientists, and Infrastructure Engineers, ensuring alignment with architectural standards and driving a culture of high quality and operational excellence.
Drive cross-functional initiatives to integrate modern Machine Learning and AI technologies (including semantic understanding, natural language processing, and potentially large language models) to automate data quality, link canonical products, and create intelligent data enrichment solutions.
Define strategies to enhance the performance, reliability, and observability of data and ML services, ensuring robust, high-quality data outputs.
Design and implement frameworks for evaluating data quality and the effectiveness of ML models through both offline metrics and online validation.
Champion engineering best practices and mentor engineers across teams, raising the bar for code quality, data governance, and ML system design.
Shape long-term technical direction by staying ahead of trends in AI, ML, data engineering, and distributed systems and bringing these innovations into production within the Knowledge domain.
Work on site for collaboration sessions, customer meetings, and internal workshops.

Extensive experience designing and leading the development of large-scale distributed data and/or ML backend systems.
Hands-on experience with ETL pipeline design and optimization for complex data sets is a strong advantage.
Deep familiarity with technologies such as Apache Beam, Pub/Sub, Redis, and other large-scale data processing frameworks.
Expertise in backend development with Python and Scala; knowledge of Node.js or Golang is a plus.
Proficient with both SQL and NoSQL databases, and experience with data warehousing solutions.
Demonstrated experience building robust APIs (REST, GraphQL) and operating in modern cloud environments (GCP preferred), using Kubernetes, Docker, CI/CD, and observability tools.
Proven ability to lead and influence engineering direction across teams and functions, particularly in a data-centric and ML-driven environment.
Strong communication skills and the ability to align diverse technical stakeholders around a cohesive vision for data quality and knowledge extraction.

Competitive base salary.
Matching pension scheme (up to 5%) from day one.
Discretionary company bonus scheme.
4 x annual salary Death in Service coverage from day one.
Employee referral scheme.
Tech Scheme.
Private medical insurance from day one.
Optical and dental cash back scheme.
Help@Hand app: access to remote GPs, second opinions, mental health support, and physiotherapy.
EAP service.
Cycle to Work scheme.
36 days annual leave (inclusive of bank holidays).
An extra paid day off for your birthday.
Ten paid learning days per year.
Flexible working hours.
Market-leading parental leave.
Sabbatical leave (after five years).
Work from anywhere (up to 3 weeks per year).
Industry-recognised training and certifications.
Bonusly employee recognition and rewards platform.
Clear opportunities for career development.
Length of Service Awards.
Regular company events.