At a Glance
- Tasks: Build and implement AI systems from the ground up, ensuring robust performance.
- Company: Join a forward-thinking tech company focused on AI innovation.
- Benefits: Enjoy competitive pay, flexible working options, and opportunities for growth.
- Other info: Dynamic team environment with excellent career advancement potential.
- Why this job: Make a real impact by integrating ML into cutting-edge backend systems.
- Qualifications: Experience in AI/ML engineering and strong coding skills required.
The predicted salary is between 60000 - 80000 £ per year.
Your Opportunity
- Build production AI systems: Design and implement the full stack, from FastAPI endpoints that handle requests, to training pipelines that process data, to inference services that serve predictions. You'll own the architecture, not just the model weights.
- Train and deploy our DSLM: Fine-tune models using Unsloth/Axolotl, but more importantly, build the robust infrastructure around it - data pipelines that feed training, evaluation frameworks that catch regressions, deployment systems that handle failover. Make it production-grade.
- Integrate ML into our backend: We use FastAPI, PydanticAI, FastMCP, Memgraph. You'll extend these systems with ML capabilities, not as a separate 'ML service' but as a natural part of our backend architecture. Clean abstractions, proper error handling, observability.
- Own inference performance: Get models running fast, whether that's vLLM deployment, quantization strategies, batching optimizations, or caching.
AI/ML Engineer employer: Kallikor
Join a forward-thinking company that prioritises innovation and collaboration, where as an AI/ML Engineer, you'll have the opportunity to shape cutting-edge production AI systems in a dynamic work environment. Our culture fosters continuous learning and professional growth, offering access to the latest technologies and methodologies, while our commitment to employee well-being ensures a supportive atmosphere that values work-life balance. Located in a vibrant tech hub, we provide unique advantages such as networking opportunities with industry leaders and a chance to contribute to impactful projects that drive real change.
StudySmarter Expert Advice🤫
We think this is how you could land AI/ML Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI/ML space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving FastAPI and ML integrations. We love seeing real-world applications of your work, so make sure to highlight your best stuff!
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and system design. We recommend practicing with mock interviews or coding challenges to get comfortable with the types of questions you might face.
✨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’re always on the lookout for passionate candidates who want to build production-grade AI systems.
We think you need these skills to ace AI/ML Engineer
Some tips for your application 🫡
Show Your Passion for AI/ML:When you're writing your application, let us see your enthusiasm for AI and machine learning. Share any personal projects or experiences that highlight your skills and interest in the field. We love seeing candidates who are genuinely excited about what they do!
Tailor Your Application:Make sure to customise your application to fit the job description. Highlight your experience with FastAPI, data pipelines, and any relevant tools like Unsloth or Axolotl. We want to see how your background aligns with our needs, so don’t hold back on the specifics!
Be Clear and Concise:Keep your application straightforward and to the point. Use clear language and avoid jargon unless it’s relevant to the role. We appreciate a well-structured application that makes it easy for us to see your qualifications at a glance.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it helps us keep everything organised on our end!
How to prepare for a job interview at Kallikor
✨Know Your Tech Stack
Familiarise yourself with the technologies mentioned in the job description, like FastAPI and PydanticAI. Be ready to discuss how you've used these tools in past projects or how you would approach integrating ML into a backend system.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in building AI systems. Highlight your thought process and the steps you took to overcome obstacles, especially around training pipelines and deployment systems.
✨Demonstrate Your Ownership Mindset
Emphasise your ability to take ownership of projects. Discuss instances where you’ve designed architecture or built infrastructure from scratch, showcasing your understanding of what it means to own the entire stack, not just the model.
✨Prepare for Technical Questions
Expect questions on inference performance and optimisation strategies. Brush up on concepts like quantization and batching, and be ready to explain how you would implement these in a production environment to ensure models run efficiently.
