At a Glance
- Tasks: Transform AI models into robust software for impactful public sector projects.
- Company: Join Zaizi, a forward-thinking tech company focused on solving complex challenges.
- Benefits: Competitive pay, generous leave, and fully funded professional development.
- Other info: Inclusive culture with opportunities for mentorship and career growth.
- Why this job: Make a real difference in the UK's digital infrastructure while growing your skills.
- Qualifications: Experience in machine learning and software engineering required.
The predicted salary is between 72000 - 88000 £ per year.
hackajob is partnering directly with Zaizi to hire for this role.
Role Overview
- Dept: Applied AI Team
- Term: Permanent
- Location: Cheltenham
- DV Clearance: Required & British
Work on exciting public sector projects and make a positive difference in people's lives. At Zaizi, we thrive on solving complex challenges through creative thinking and the latest tools and tech. As a Machine Learning Implementation Engineer, you'll be responsible for taking AI models and algorithms from the research phase and maturing them into robust, production-ready software. Your focus is on increasing the Technology Readiness Level (TRL) of our AI solutions, ensuring they are scalable, reliable, and effectively integrated into business workflows to automate tasks and gain insights from data.
Key responsibilities include:
- Refining model code for performance
- Ensuring software maturity
- Solving complex technical hurdles during the transition from prototype to product
- Influencing technical decisions within the team
- Mentoring junior members
- 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.
Requirements
Role Objectives
- Model Maturation & Delivery: Design, build, and refactor complex machine learning models, ensuring they move from experimental stages to high-quality, high-performance production software.
- Software Engineering for AI: Take the output from research activities and "harden" the software to achieve higher TRLs suitable for deployment in the National Security Domain.
- System Integration: Design and manage the integration of ML models into broader software architectures, ensuring stability and performance.
- Edge Optimization & Deployment: Adapt and optimize machine learning models for resource-constrained environments, ensuring high-performance execution on edge hardware, including Android-based mobile devices.
- Technical Excellence: Stay current with AI implementation best practices to ensure our solutions are maintainable and scalable.
Skills & Experience
- Technical Expertise: Broad technical expertise in machine learning, demonstrating a deep understanding of ML algorithms, frameworks, and software engineering best practices.
- Model Maturation: Proven experience in taking research-grade AI/ML code and maturing it into production-ready software.
- Technology Implementation: Ability to evaluate new generative AI technologies for business relevance and feasibility, focusing on practical implementation within the National Security Domain.
- Advanced Prototyping: Delivering robust proofs of concept that bridge the gap between initial research and final application.
- Specialist Advice: Serving as a key technical resource for the practical application of generative AI within the organization.
- Data Science: Applying a range of data science techniques to support model refinement and software hardening.
- Deployment Experience: Proven experience in building and deploying complex machine learning models into live environments.
Nationality
Due to UK government security restrictions, you will need to be a British Citizen and have been resident in the UK for the last ten years to apply for this role.
Benefits
- Competitive Pay: Salaries reviewed annually to ensure they reflect your performance and market value.
- Loyalty Pension: We invest in your future. Starting at a 5% employer contribution, we increase this by 0.5% every year after your third anniversary, up to a maximum of 8%.
- Protection: Comprehensive Group Life Assurance for peace of mind.
Purpose & Culture
- Real Impact: Work on mission-critical projects that secure and improve the UK's digital infrastructure.
- Autonomy: A culture that empowers you to make decisions, prototype rapidly, and iterate towards success.
- Service & Community: We support those who serve. 10 paid days for Reservist Military Service.
Work / Life Balance
- Time Off: 25 days annual leave + Bank Holidays, with the flexibility to Buy/Sell additional days to suit your lifestyle.
- Giving back: 2 paid volunteering days per year.
Development & Growth
- Master Your Craft: Fully funded professional certifications (AWS, GCP, Agile, etc.) supported by 5 days paid study leave.
- Expand Your Horizons: An additional £500 annual "Personal Choice" fund to learn whatever inspires you work-related or not.
- Support: Access to 1-2-1 professional coaching and team training to accelerate your career.
Health & Balance
- Premium Health: Vitality Private Medical Insurance (includes Apple Watch, gym discounts, and rewards).
- Flexibility: Genuine hybrid working with a WFH equipment allowance to perfect your home setup.
- Wellbeing: Cycle to Work scheme and a commitment to sustainable, healthy working practices.
For further information contact: TPBN1_UKTJ
Machine Learning Implementation Engineer, AI (SFIA4) in Gloucester employer: Hackajob Ltd
JPMorgan Chase is an exceptional employer, offering a dynamic work environment where innovation and collaboration thrive. As a Lead Site Reliability Engineer, you will not only tackle complex challenges but also benefit from extensive professional development opportunities and a strong commitment to diversity and inclusion. Located in a global financial hub, you'll be part of a team that values your expertise and encourages a culture of continuous improvement and technical excellence.
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We think this is how you could land Machine Learning Implementation Engineer, AI (SFIA4) in Gloucester
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We think you need these skills to ace Machine Learning Implementation Engineer, AI (SFIA4) in Gloucester
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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 Ltd.
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How to prepare for a job interview at Hackajob Ltd
✨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 Ltd 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.
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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.
