At a Glance
- Tasks: Build and maintain AI infrastructure, empowering over 100 Data Scientists and Engineers.
- Company: Join a leading tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Be part of a dynamic team where your contributions truly matter.
- Why this job: Make a real impact in AI by creating tools that drive machine learning success.
- Qualifications: Experience in Python or Go, with skills in containerisation and Infrastructure-as-Code.
The predicted salary is between 60000 - 80000 £ per year.
About the role
As a Software Engineer in the AI Platform team, you will be the architect of the infrastructure that makes world-class AI possible. Working closely with the Applied AI team, you’ll be building and maintaining the data science, MLOps, and deployment tooling that empowers our team of over 100 Data Scientists and Engineers. You will take ownership of the platform that enables us to transition from complex exploration to full-stack, production-grade machine learning products, ensuring our solutions are high-performing, scalable, and seamlessly integrated into diverse client environments.
What you'll be doing:
- Taking ownership of our existing deployment and MLOps tooling to ensure our software delivery remains a significant lever for quality and reliability.
- Contributing to the continuous evolution of our technology stack, from building new features in our notebook development environments to refining model monitoring systems.
- Collaborating with a small, fast-moving team of customer-facing technologists to design and build the infrastructure our delivery teams need to succeed.
- Designing and implementing infrastructure-as-code and DevSecOps processes to support distributed, containerised microservices architectures.
- Integrating our core platform services across multiple cloud environments, including AWS, Azure, and GCP, to provide flexible solutions for our global clients.
- Scaling our internal enablement capabilities, acting as an entrepreneurial force that removes technical friction and accelerates the deployment of machine learning.
Who we're looking for:
You are a Software Engineer who is passionate about building internal tools and takes pride in creating the foundational systems that enable others to excel. You understand the nuances of the machine learning product lifecycle and have a clear vision for how to move models efficiently from exploration to production. You possess modern systems programming skills in Python or Go, and you are comfortable selecting the best-fit technology for complex infrastructure challenges. You bring practical experience with containerisation and orchestration, specifically using Docker and Kubernetes to manage distributed systems at scale. You have a strong background in Infrastructure-as-Code (IaaC) using tools like Terraform or CloudFormation, combined with a deep interest in DevSecOps practices. You thrive in small, ambitious teams where you can take high levels of ownership and communicate effectively with both technical and non-technical peers.
Software Engineer - Platform in London employer: Gravity Engineering Services Pvt Ltd.
As a leading innovator in AI technology, our company offers an exceptional work environment for Software Engineers passionate about building impactful infrastructure. Located in a vibrant tech hub, we foster a collaborative culture that prioritises employee growth through continuous learning and hands-on experience with cutting-edge tools. Join us to be part of a dynamic team where your contributions directly influence the success of world-class machine learning products, all while enjoying competitive benefits and a commitment to work-life balance.
Contact Details:
Gravity Engineering Services Pvt Ltd. Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Software Engineer - Platform in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to MLOps or infrastructure. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python or Go skills. Practice coding challenges and system design questions that relate to building scalable systems. We want you to feel confident when it’s time to shine!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Software Engineer - Platform in London
Some tips for your application 🫡
Show Your Passion:When writing your application, let your enthusiasm for software engineering and AI shine through. We want to see that you’re not just ticking boxes but genuinely excited about building tools that empower others.
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with our job description. Highlight your experience with Python or Go, containerisation, and any relevant projects that showcase your ability to build scalable systems.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you’re the perfect fit for our team. Share specific examples of how you’ve taken ownership in past roles and how you’ve contributed to the success of your projects.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process.
How to prepare for a job interview at Gravity Engineering Services Pvt Ltd.
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, Go, Docker, and Kubernetes. Brush up on your knowledge of Infrastructure-as-Code tools like Terraform or CloudFormation, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles, especially related to MLOps and deployment tooling. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight how you took ownership of projects and drove them to success.
✨Understand the Machine Learning Lifecycle
Familiarise yourself with the nuances of the machine learning product lifecycle. Be ready to explain how you would transition models from exploration to production, and share any relevant experiences that demonstrate your understanding of this process.
✨Communicate Effectively
Since you'll be collaborating with both technical and non-technical peers, practice explaining complex concepts in simple terms. This will show your ability to bridge the gap between different teams and ensure everyone is on the same page.
