At a Glance
- Tasks: Design and deploy cutting-edge AI systems that drive real productivity.
- Company: Join a forward-thinking professional services firm with a focus on innovation.
- Benefits: Enjoy private medical insurance, flexible working, and generous holiday entitlement.
- Other info: Dynamic environment with opportunities for personal and professional growth.
- Why this job: Make a tangible impact in the evolving AI landscape while collaborating with diverse teams.
- Qualifications: 6+ years in software engineering, strong Python and SQL skills required.
The predicted salary is between 80000 - 90000 £ per year.
Location: Hybrid Working – London N3 / EC4M
Working Hours: Monday to Friday, 35 hour week (Flexitime)
Reporting to: Operations & Transformation Director
Salary: £80,000 - £90,000
About the Team: You’ll be joining our Strategy and Transformation Team, working at the heart of BKL’s AI capability. The team works closely with senior leadership, operational stakeholders and colleagues across service lines, supporting them with technology delivery, strategic change, and digital transformation. We’re collaborative, innovative and outcome‑focused, offering exposure to firm‑wide transformation initiatives and the opportunity to help shape our evolving data and AI capabilities.
We’re looking for a hands‑on and versatile Data & AI engineer who’s excited to ship production AI systems in a professional services firm. You’ll work closely with senior stakeholders to scope opportunities to use AI and then partner with teams to develop point solutions for these. The solutions will range in nature from intelligent document processing to agentic workflows that augment our advisors and drive real productivity. If you’re pragmatic, curious, and motivated by impact, we’d love to hear from you.
Responsibilities:
- Design, build, and deploy production‑grade data and AI systems, from ingestion and transformation pipelines through to the AI applications that run on them.
- Build and maintain reliable data pipelines and lakehouse infrastructure that serve both analytics and AI workloads across the firm.
- Own technical decisions across the stack – from data modelling and pipeline design to retrieval, evaluation and deployment patterns.
- Build robust evaluation frameworks to measure and improve system quality over time.
- Scope new opportunities and gather requirements with business teams, capturing and prioritising requirements and defining what good looks like before building.
- Partner closely with teams across the firm as a trusted adviser, running discovery to understand how they work, analysing their processes and pain points, and shaping and scoping point solutions together that deliver measurable business value.
- Contribute to our engineering standards, mentoring others and raising the bar on code quality, testing, and operational excellence.
- Stay close to the rapidly evolving AI ecosystem and bring relevant innovations into our work.
Required Skills & Experience:
- 6+ years of professional software engineering experience, with a track record of developing production‑grade systems.
- Strong expertise in Python and SQL.
- Solid understanding of data engineering foundations – orchestrating ETL/ELT pipelines, data modelling, and working with large datasets in a lakehouse/warehouse setting.
- A genuinely collaborative, business‑facing approach – comfortable acting as the bridge between business and technology: running discovery with non‑technical teams, gathering and analysing requirements, and translating business problems into scoped, high‑impact AI solutions together.
- Hands‑on experience building LLM‑powered applications, with strong underlying skills (prompt engineering, structured outputs, tool use and evaluation).
- Experience building agentic workflows or retrieval‑augmented (RAG) applications.
- Strong CI/CD and production deployment discipline – testing, observability, versioning and safe rollout.
- Experience with Databricks (Unity Catalog, Delta, PySpark) or equivalent lakehouse platforms.
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related STEM field.
Desired Skills & Experience:
- Experience building on Microsoft or Azure AI platforms (such as Azure AI Foundry, Copilot Studio or the Power Platform), or the appetite to get up to speed quickly.
- Familiarity with agent and tool‑integration patterns – function and tool calling, connecting agents to enterprise data and systems, and protocols such as MCP.
- Comfort working across multiple LLM providers and selecting the right model for a given task.
- Experience delivering user‑facing tools within enterprise environments such as Microsoft 365, Teams or the web.
- Fluency with AI‑assisted development tooling in day‑to‑day development.
- Background in professional services, consultancy or another regulated or advisory environment.
- Strong communication skills, with the ability to explain complex technical concepts clearly to non‑technical stakeholders.
Benefits:
- Private Medical Insurance (PMI) – including mental health cover, hospital treatment & 24/7 GP access
- Health Cash Plans – covering everyday costs like dental, optical & physiotherapy and an Employee Assistance Programme
- Pension scheme – helping you save for retirement in a tax‑efficient way
- Group Life Assurance – financial protection for your loved ones
- Cashback & savings portal – discounts across hundreds of high‑street and online retailers
- Cycle to Work Scheme – spread the cost of a new bike and accessories tax‑free
- Electric Vehicle Scheme – sustainable transportation options that include roadside support and servicing
- Free Mortgage Advice – expert guidance for your home‑buying journey
- Season Ticket Loan – support with travel expenses
- Enhanced Family Leave – generous leave policies for family‑related needs
- Holiday Entitlement – generous entitlement which increases with promotion
Compliance & Equal Opportunities: In addition to the responsibilities listed above, you will be required to undertake GDPR training to fully understand the regulations and ensure all work is carried out in a compliant manner. BKL is an equal opportunities employer and positively encourages applications from suitably qualified candidates eligible to work in the UK. Inclusion, diversity and talent development are a fundamental aspect of who we are, and we welcome applications from candidates who share and champion these values. By applying, you consent to the processing of your personal data for recruitment purposes, in line with UK GDPR. Your information will be used securely, only for recruitment, and won’t be shared without your consent. For details, see our Privacy Policy – BKL.
Senior AI and Data Engineer employer: BKL
BKL is an exceptional employer that fosters a collaborative and innovative work culture, particularly within our Strategy and Transformation Team in London. We offer flexible working hours, competitive salaries, and a comprehensive benefits package, including private medical insurance and enhanced family leave, all while providing ample opportunities for professional growth and development in the rapidly evolving field of AI and data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Senior AI and Data Engineer
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like BKL.
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We think you need these skills to ace Senior AI and Data Engineer
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!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at BKL, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at BKL. 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!
How to prepare for a job interview at BKL
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at BKL!
✨Prepare for Case Studies
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.
