Principal Machine Learning Engineer job opportunity at Qodea.

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Date2025-12-11T14:12:09.924Z bot
Qodea Principal Machine Learning Engineer
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Pattern: Full-time
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loacation London, United Kingdom
loacation London....United Kingdom

Work where work matters. Elevate your career at Qodea, where innovation isn't just a buzzword, it's in our DNA. We are a global technology group built for what's next, offering high calibre professionals the platform for high stakes work, the kind of work that defines an entire career. When you join us, you're not just taking on projects, you're solving problems that don't even have answers yet. You will join the exclusive roster of talent that global leaders, including Google, Snap, Diageo, PayPal, and Jaguar Land Rover call when deadlines seem impossible, when others have already tried and failed, and when the solution absolutely has to work.

 Forget routine consultancy. You will operate where technology, design, and human behaviour meet to deliver tangible outcomes, fast. This is work that leaves a mark, work you’ll be proud to tell your friends about.

 Qodea is built for what’s next. An environment where your skills will evolve at the frontier of innovation and AI, ensuring continuous growth and development.

 We are looking for a Principal Machine Learning Engineer to shape the next generation of our data and machine learning capabilities, focusing on data quality, enrichment, and the intelligent linking of products and information. This role offers the opportunity to define architectural strategy, lead transformative initiatives, and work at scale on platforms infused with machine learning and semantic intelligence to unlock deep insights from complex data. We look for people who embody: Innovation to solve the hardest problems. ‍ Accountability for every result. ‍ Integrity always. About The Role 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. This role is designed for impact, and we believe our best work happens when we connect. While we operate a flexible model, we expect you to spend time on site (at our offices or a client location) for collaboration sessions, customer meetings, and internal workshops. What Success Looks Like 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.

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