AI Engineering Lead – SVP (Hybrid)

Storm Global Analytics
1 month ago
Applications closed

Related Jobs

View all jobs

GTM Engineer (Sales Operations Department)

Technical Lead

Lead Generative AI Engineer

Lead Data Engineer

Lead Software Engineer

Product Manager

Generative AI Engineering Lead – SVP

A start-up with the exciting clients mission of shaping the direction of travel for the group by defining the tech and engineering strategy for the bank. We are a team of talented engineers, product managers and tech SMEs, taking ambiguous concepts and making them real by engineering cutting edge products at planetary scale! We are solely focused on the most modern technology and engineering disciplines such as generative AI, cloud, security, modern app stacks (with Golang, Gatekeeper), open source and the latest and greatest in the Kubernetes ecosystem.
Generative AI is a relatively new and growing space, as a result, we ask that you share with us a specific AI engineering projects on LLMs that you’re proud of in your application. Ideally these projects should show off complex and clever architectures or a systematic evaluation of an LLM’s behaviour.

You might be a good fit if you:

  • Seize the opportunity to explore gen AI, machine learning and its real-world applications at scale. Jump in!
  • Rapidly prototype ideas, approaches and methods in the AI space.
  • Read, implement and improve the latest papers in the AI field.
  • A relentless passion to learn more and go deep into machine learning and generative AI concepts, bringing your research to engineer products that shape our client’s future.
  • Be a game-changer, ready to step beyond your designated role.
  • Bring your deep-dive software engineering expertise.
  • Thrive in a results-driven environment, where flexibility fuels impact.
  • Love the synergy of pair programming? So do we!

What you’ll do within the Tech Strategy team:

  • Lead the research and conceptual builds of Generative AI products.
  • Design and build high-quality, highly reliable products with user experience at the centre.
  • Be responsible for turning research insight into best-in-class AI platforms for the bank.
  • Creating firsts in the Generative AI space for our client as part of the team that defines the strategic direction for the bank.
  • Continually iterate and scale Generative AI products, whilst continually researching the advancements in technology and listening to the needs of the customers (internal).
  • Mentor and nurture other engineers to help them grow their skills and expertise.

Experience that will help you succeed in this role:

  • A well-known contributor to open source and always thinking out of the box tooling, using and standardizing with methods of creating APIs, ML/Ops automation and more.
  • Have experience supporting fast-paced startup engineering teams.
  • Understanding of language models and transformers.
  • Have used LangChain and even contributed to the project.
  • Fluency in at least two programming languages, with preference for Python, JavaScript/Typescript, Golang.
  • Experience designing control and sandboxing systems for AI research.
  • Pytorch or TensorFlow experience.
  • Experience maintaining and/or contributing to bug bounty and responsible disclosure programs.
  • Rich understanding of vector stores and search algorithms.
  • Large-scale ETL development.
  • Direct engineering experience of high performance, large-scale ML systems.
  • Deep hands-on knowledge of Kubernetes, developing backend platforms and engineering APIs that scale.
  • Hands-on MLOps experience, with an appreciation of the end-to-end CI/CD process.

What we believe in:

  • We do not have boundaries between engineering and research, and we expect all our technical staff to contribute to both as needed.
  • We take a product-focused approach and care about building solutions that are robust, scalable, and easy to use.
  • We enjoy working in a fast-paced team tackling cutting-edge problems by constantly testing and learning.
  • We enjoy pair programming for our products, we are lean in our approach and remove bureaucracy where we see it.
  • We believe in delivering fast, iterating and pivoting as we go, rather than defining the perfect solution upfront.

#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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.