Principal AI Engineer (London, hybrid)

IPV Curator
London
2 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist / AI Engineer

Principal Data Scientist - Marketing

Principal Data Scientist - Remote (Basé à London)

Principal Data Scientist (Remote)

Principal Data Scientist (Remote)

Data Engineer

Principal AI Engineer (London, hybrid)Kittl

London, GB
Apply for this job

  • Job Type:Full-Time
  • Function:Data Science
  • Industry:AI
  • Post Date:02/26/2025
  • Website:www.kittl.com
  • Company Address:, Berlin , DE

About Kittl

Kittl is the most intuitive design platform for creators. Creating beautiful designs can be a long and painful process. We decided to tackle this problem heads-on and have built Kittl.com to provide an alternative to professional tools.

Job Description

We're in an exciting growth phase following our Series B funding and are expanding our team. Our hybrid working culture includes three office days per week (Monday, Wednesday, and Friday) at our Berlin headquarters. Looking ahead, we're also growing London as our second location, with a new office planned for 2025.

Your role at Kittl

As a Principal AI Engineer at Kittl, you will lead the AI Platform team, driving AI initiatives across the company and ensuring seamless integration of state-of-the-art models into our product ecosystem. Your work will directly influence the development of transformative features, optimizing workflows, and scaling AI-powered capabilities, specifically for graphic design. You will collaborate closely with product and business stakeholders to align AI advancements with strategic goals while maintaining and evolving the internal AI infrastructure. You will report to the CTO.

What you'll do

  • Lead AI infrastructure development: Architect, maintain, and evolve the internal AI inference infrastructure to support scalable and efficient model deployment.
  • Drive AI strategy & roadmap: Develop and execute the technical roadmap for AI at Kittl, aligning it with business objectives while identifying opportunities for AI-driven innovation across the company.
  • Enable cross-team AI adoption: Establish processes and best practices to help product managers and engineers uncover AI opportunities, fostering AI-driven decision-making across teams.
  • Optimize AI performance & efficiency: Ensure robust, scalable, and cost-effective AI inference by optimizing model deployment, resource allocation, and infrastructure scalability.
  • Bridge product & business needs: Work closely with product and business teams to translate high-level goals into actionable AI solutions, ensuring AI initiatives deliver measurable impact.

What you'll need

  • Experience with Gen AI & deep learning: At least 5 years of demonstrated success in deploying and optimizing deep learning models in production, with a focus on diffusion models and LLMs.
  • Leadership: At least 2 years in a leadership role guiding teams and defining AI strategy.
  • Infrastructure & scalability expertise: Strong knowledge of AI infrastructure, cloud platforms (AWS, GCP, or Azure), and model optimization techniques for efficient deployment.
  • Strategic & leadership skills: Ability to define and execute an AI strategy, aligning technical roadmaps with business goals while guiding cross-functional teams.
  • Hands-on technical skills: Proficiency in Python, Pytorch, Diffusers, and distributed computing frameworks.
  • Strong communication & collaboration: Ability to convey complex AI concepts to technical and non-technical stakeholders, driving alignment and adoption across teams.
  • Continuous learning & adaptability: Stay up-to-date with the latest advancements in AI, ensuring Kittl remains at the forefront of innovation in a rapidly evolving field.

We are looking for someone

  • Exceptionally driven to drive impact and challenge the status quo.
  • Who takes extreme ownership & gets things done.
  • Who goes above and beyond in their role.
  • Who is deeply passionate about what they do.

Benefits

  • Maximise your impact: No matter if you're leading a team or you stand out by your domain expertise - all we care about is supporting you to maximise your own impact.
  • Hackathons: Our quarterly hackathons provide an environment to experiment with new concepts, push boundaries, and potentially deliver the next big thing.
  • Kittl Week: Each year, our global team gathers together for a whole week, to work, celebrate, get inspired, and have fun.
  • Flexible working hours: Our core hours are 11am-5pm CET, leaving the rest of your schedule flexible to fit your style.
  • Workspace access: Premium WeWork All Access account, enabling you to work from any global WeWork location.
  • Remote work: Work up to 50 days (10 weeks) fully remote per year from anywhere in the world, as long as you maintain our core hours.
  • Learning & development: Our L&D budget supports your professional growth.
  • Vacation: Up to 30 vacation days per year.

At Kittl, we embrace diversity and value every team member's unique background, identity, and experience. We're all about respect, honesty, and inclusivity. Together, we create a safe and supportive work environment where everyone thrives. Join us on this exciting journey of making our company and product even better!

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Jobs Careers

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Jobs

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.