At a Glance
- Tasks: Design and develop cutting-edge machine learning models to solve real-world problems.
- Company: Dynamic digital consultancy focused on public sector innovation.
- Benefits: Inclusive culture, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and career advancement.
- Why this job: Make a positive impact while working with the latest AI technologies.
- Qualifications: Strong background in machine learning and problem-solving skills.
The predicted salary is between 63000 - 77000 £ per year.
Not sure what skills you will need for this opportunity Simply read the full description below to get a complete picture of candidate requirements.
Our client is a digital and technology consultancy serving the public sector.
Work on exciting public sector projects and make a positive difference in people's lives.
We thrive on solving complex challenges through creative thinking and the latest tools and tech.
As a Machine Learning Engineer, AI, you'll be responsible for researching, developing, and testing new AI algorithms, models, and technologies that businesses can use to automate tasks and gain insights from their data.
Key responsibilities include building complex models, designing and managing MLOps pipelines for CI/CD, monitoring, and model retraining.
Mentoring junior members, influencing technical decisions within the team, and handling complex, non-routine problems.
Our work culture is inclusive, modern, friendly, and democratic.
We look for bright, positive-thinking individuals with a can-do attitude.
Our people enjoy challenging themselves to be the best at what they do – if that sounds like you, you'll fit right in!
Role Objectives
These are the expected objectives for this role. We are happy to discuss this further during the interview process with the successful candidate.
- Model Development & Delivery: Design, build, test, and deploy complex machine learning models, ensuring high standards of quality, performance, and scalability Decide what model is most suitable for use in products and services
- MLOps
- Pipeline
Management: Design and manage robust MLOps pipelines, including continuous integration/continuous delivery (CI/CD), monitoring, and model retraining, to ensure efficient and reliable model deployment and operation
- Advanced
- Problem
Solving: Act as a technical expert for complex, non-routine technical challenges within machine learning, developing and implementing innovative and effective solutions
- Customise, optimise, re-train and maintain existing models
- Deploy models into production, testing and assuring them to ensure they meet performance requirements
- Work with others to integrate models with existing systems
- Check that models used in live products and services stay safe, secure and continue to work effectively
Requirements
- Broad technical expertise in machine learning, demonstrating a deep understanding of various ML algorithms, frameworks, and best practices.
- Research. Plans and directs and carries out research activities, acting as a subject matter expert in generative AI research.
- Emerging Technology Monitoring.
Systematically discovers and evaluates new generative AI technologies for business relevance, feasibility and relevance within the National Security Domain.
- Prototyping. Delivers complex, high-risk proofs of concept that test new AI applications.
- Specialist Advice. xsabvtc Serves as the primary source of expertise for generative AI within the organization.
Lead Machine Learning Engineer in Whitehall employer: Hackajob
Joining Google as a Security Platform Engineer in the UK Public Sector means becoming part of a dynamic and innovative team dedicated to delivering secure private cloud services for critical customers. With a strong emphasis on employee growth, you will have access to cutting-edge technology and collaborative opportunities that foster professional development. The inclusive work culture at Google encourages creativity and teamwork, making it an exceptional employer for those seeking meaningful and impactful work in a supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Machine Learning Engineer in Whitehall
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Hackajob or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Hackajob.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Hackajob.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Hackajob that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Lead Machine Learning Engineer in Whitehall
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Hackajob.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Hackajob and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Hackajob
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Hackajob uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
