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- Tasks: Lead AI and machine learning projects while managing a talented team.
- Company: Join a forward-thinking company focused on data-driven solutions.
- Benefits: Enjoy remote work flexibility, competitive salary, bonuses, and perks.
- Why this job: Be a key player in shaping a data culture and driving innovation.
- Qualifications: Experience in AI products, cloud services, and strong leadership skills required.
- Other info: Monthly office visits in London for team collaboration.
Lead ML Engineer London – Hybrid (1 day per week in office) £80,000 – £100,000 DOE + Bonus (up to 15%) + Excellent Pension + Car Scheme + Technology Benefits + EAP Programme + Flexible working This is an incredible opportunity for a Lead ML Engineer to join a fast paced and forward-thinking business always looking to innovate and lead from the front in the technology world. The company are a leading organisation in the energy sector, dedicated to delivering innovative solutions and improving operational efficiency. As part of their Data Science team, you will be at the forefront of cutting-edge projects, helping to shape the future of data-driven decision-making and machine learning infrastructure. In this role, you will lead machine learning projects from concept to production, develop platform tools, and collaborate with data scientists to build data pipelines. You'll mentor junior team members, work with IT teams to advance projects, and improve deployment processes. Additionally, you'll design and maintain cloud infrastructure, ensure high-quality code, and participate in code reviews. The ideal candidate will have proven experience in a similar role in some combination of Software, Data or DevOps and strong experience working with ML models. You will be an expert with Python and associated libraries (Pandas, scikit-learn etc.), have strong Azure DevOps exposure (Terraform, Docker, Kubernetes) and high proficiency in SQL. An incredible opportunity for a confident and commercial ML Engineer to lead from the front working with cutting edge technology and driving company growth. The Role: Lead machine learning projects from concept to production. Develop platform tools and collaborate with data scientists to build data pipelines. Mentor junior team members and support their technical growth. Work closely with IT teams to advance project goals and improve deployment processes. Design and maintain cloud infrastructure to support machine learning initiatives. The Person: Proficiency in Python, including libraries such as Pandas and scikit-learn, and strong SQL skills. Deep understanding of software engineering best practices Experience with tools like Azure, Azure ML, GitHub Actions, Terraform, Packer, Airflow, Docker, and Kubernetes Expertise in Linux/Windows VM administration. Reference Number: BBBH(phone number removed) To apply for this role or to be considered for further roles, please click "Apply Now" or contact Ryan McIntyre at Rise Technical Recruitment. Rise Technical Recruitment Ltd acts an employment agency for permanent roles and an employment business for temporary roles. The salary advertised is the bracket available for this position. The actual salary paid will be dependent on your level of experience, qualifications and skill set. We are an equal opportunities employer and welcome applications from all suitable candidates
Lead Machine Learning Engineer employer: CV-Library
Contact Detail:
CV-Library Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Machine Learning Engineer
✨Tip Number 1
Make sure to showcase your leadership experience in machine learning projects. Highlight specific instances where you led a team or collaborated with cross-functional groups, as this is crucial for the role.
✨Tip Number 2
Familiarize yourself with the latest trends and technologies in AI and machine learning, especially those related to cloud-based services like GCP. Being able to discuss these topics confidently can set you apart during interviews.
✨Tip Number 3
Prepare to discuss how you've contributed to building a 'data culture' in previous roles. Think of examples where you demonstrated the value of data in decision-making processes.
✨Tip Number 4
Network with professionals in the field of machine learning and data science. Engaging with communities on platforms like LinkedIn can help you gain insights and potentially get referrals for the position.
We think you need these skills to ace Lead Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in leading machine learning projects and teams. Emphasize your skills in AI, cloud-based services, and relevant programming languages like Python.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data science and your vision for developing a 'data culture'. Mention specific examples of how you've successfully led teams and delivered AI-based products.
Showcase Relevant Projects: Include details about specific projects where you implemented machine learning solutions. Highlight your role, the technologies used, and the impact these projects had on business goals.
Prepare for Technical Questions: Be ready to discuss your technical expertise in machine learning algorithms and cloud services during interviews. Prepare to explain your approach to deploying ML services and managing teams effectively.
How to prepare for a job interview at CV-Library
✨Showcase Your Leadership Experience
Since the role requires leading a team, be prepared to discuss your previous leadership experiences. Share specific examples of how you've successfully managed teams and projects, highlighting your ability to foster collaboration and drive results.
✨Demonstrate Technical Proficiency
Make sure to highlight your experience with AI and machine learning technologies. Be ready to discuss specific projects where you implemented classical and modern algorithms, and how you utilized cloud services like GCP in your work.
✨Communicate Effectively
Strong communication skills are essential for this role. Practice explaining complex technical concepts in simple terms, as you'll need to translate between business and data teams. Prepare to discuss how you've effectively communicated recommendations and results in past projects.
✨Emphasize Your Data Culture Advocacy
The company values a strong data culture, so be prepared to discuss how you've contributed to fostering a data-driven environment in previous roles. Share your thoughts on the importance of data in decision-making and how you've championed data science initiatives.
