Tech Lead (Gen AI) - UK

Photon
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer (SC Cleared)

Data Analyst

SAS Data Engineer

Data Analyst

Lead Data Engineer

Lead Data Engineer

Key Responsibilities: 

Lead the creation of a POC for using generative AI in software development, demonstrating its potential to improve efficiency, quality, and innovation.  Collaborate with software development teams to integrate generative AI solutions into existing workflows.  Lead and mentor a team of data engineers, providing technical guidance and fostering a collaborative team environment.  Design, develop, and implement generative AI applications using Python.  Work closely with stakeholders to understand business requirements and translate them into technical solutions.  Drive the development and execution of data engineering strategies to support business objectives.  Develop and maintain scalable data pipelines, API and model deployments, and workflows.  Communicate complex analytical findings and insights to non-technical stakeholders in a clear and concise manner.  Stay current with industry trends, best practices, and emerging technologies in data analytics, generative AI, and Python programming. 

Qualifications: 

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.  5+ years of experience in data engineering and AI, with at least 2 years in a leadership or supervisory role.  Proficiency in Python programming and its libraries (. pandas, numpy, scikit-learn, matplotlib, seaborn).  Experience with cloud platforms (., AWS, Azure, Google Cloud) and their data analytics and AI services.  Strong understanding of statistical analysis, machine learning, and data mining techniques.  Experience with generative AI models (., GPT, GANs) and their applications in software development.  Experience with big data technologies (., Hadoop, Spark) and data visualization tools (., Tableau, Power BI) is a plus.  Solid experience with SQL and database management.  Excellent problem-solving skills and the ability to work independently and as part of a team.  Strong communication and interpersonal skills, with the ability to effectively convey technical concepts to non-technical stakeholders. 

Preferred Qualifications: 

Knowledge of data governance and data privacy regulations.  Experience in agile development methodologies. 

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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

Industry Insights

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

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 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.