At a Glance
- Tasks: Lead the architecture for AI-driven data platforms and pipelines, shaping innovative solutions.
- Company: Join a cutting-edge tech company at the forefront of marketing and advertising technologies.
- Benefits: Flexible hybrid or remote work options, competitive salary, and opportunities for professional growth.
- Other info: Dynamic environment with mentorship opportunities and a culture of innovation.
- Why this job: Make a real impact in AI technologies while collaborating with talented teams.
- Qualifications: 8+ years in data systems, strong Python skills, and experience with LLMs.
The predicted salary is between 60000 - 80000 £ per year.
About the Position
As a Staff AI Engineer, you’ll serve as a technical leader for our LLM-powered products at the forefront of marketing and advertising technologies. You’ll own critical architectural decisions, set quality bars, and lead multi-team initiatives that drive measurable outcomes. You will lead the vision and execution of our data platform and LLM-powered products, turning high-level product and business requirements into robust, scalable data products that drive measurable outcomes for our Fortune 500 customers.
This role spans data backend and ML engineering: from the reliability and cost efficiency of our pipelines to the outcome and performance of LLM-enabled features. You’ll raise the bar for data literacy across the department, craft and collaboration. You’ll be accountable for the health, cost, and evolution of key data products and data platforms, partnering closely with full-stack engineers, product, design, and devops to deliver outcomes our customers can trust. This role presents an exciting opportunity to shape the future of AI-driven technologies and make meaningful contributions to real-world applications. The role is linked with our location in London, but we are flexible about hybrid or remote work in The United Kingdom.
What You’ll Be Doing
- Lead end-to-end architecture for data platforms and pipelines: scraping, data extraction, transformation, storage, serving, and ML/LLM integration, balancing performance, reliability, security, and cost.
- Incrementally scale pipelines and systems: design safe rollout plans and north star data-quality metrics to handle customer and traffic growth without impacting production.
- Translate business goals into actionable data products: assess high-level requirements, carve clear problem spaces, draft crisp RFCs, and sequence work into deliverable projects for the team.
- Establish and enforce engineering standards: testing strategy, evals, observability, data contracts, and security practices across services.
- Think through short-term and long-term goals to come up with fast go-to-market products, while planning ahead for productization.
- Up-level the org: lead architecture reviews, codify patterns, mentor Senior Engineers, and multiply impact through documentation, code reviews, and pairing.
- Startup-ready: flexible, comfortable with ambiguity and constant change; proactive about process, documentation, and reliability without over-engineering.
- Lead the collaboration and define how AI engineers work cross-functionally with software engineers, devops, product managers and designers, to conceptualize and shape innovative and impactful solutions.
- Provide mentorship to junior team members and cultivate a culture of collaboration and innovation.
- Ship meaningful experiments: prototype data/ML capabilities, evaluate feasibility and ROI, and make pragmatic calls on productionalizing with an eye on operating costs and risk.
Qualifications
- 8+ years building and operating production data systems, including leading cross-cutting architectural changes, and deploying LLMs in real-world scenarios at scale.
- Deep experience Python and modern service architectures; strong system design and data modeling fundamentals.
- Extensive experience with training and deploying machine learning models, particularly within the NLP/LLM domain.
- Proficiency in Python.
- Familiarity with infrastructure as code, CI/CD, and cloud infrastructure.
- Fluency in operational maturity: SLOs, on-call/incident practices, and observability.
- Strong analytical and problem-solving abilities, with a bias towards action and outcomes.
- Experience with data preprocessing, feature engineering, and model evaluation techniques.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Demonstrated leadership experience, with the ability to guide and inspire a team.
Staff AI Engineer in London employer: Bluefish Labs
As a Staff AI Engineer at our London-based company, you will be part of a dynamic and innovative team that is shaping the future of AI-driven technologies in marketing and advertising. We pride ourselves on fostering a collaborative work culture that encourages mentorship and professional growth, offering flexible hybrid or remote work options to support work-life balance. With a focus on meaningful contributions and cutting-edge projects, we provide our employees with the opportunity to lead impactful initiatives while working alongside talented professionals in a supportive environment.
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We think this is how you could land Staff AI Engineer in London
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We think you need these skills to ace Staff AI Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Bluefish Labs. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Get Comfortable with Python and R
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.