At a Glance
- Tasks: Build AI systems and integrate ML into our backend architecture.
- Company: Kallikor, a leader in supply chain intelligence through AI.
- Benefits: Competitive salary, flexible work options, and opportunities for growth.
- Other info: Dynamic team environment with a focus on innovation and collaboration.
- Why this job: Join us to shape the future of supply chains with cutting-edge AI technology.
- Qualifications: Experience in Python, ML, and building production-quality systems.
The predicted salary is between 60000 - 80000 € per year.
At Kallikor, we're building the future of supply chain intelligence through AI-powered simulation digital twins. We create living digital representations of real-world operations (warehouses, distribution networks, global logistics) that help organisations make better decisions faster. We're at an inflection point: moving from AI-assisted tools to domain-specific AI that understands supply chains as deeply as our best engineers do.
You'll be instrumental in building our first domain-specific language model (DSML) and the foundation for Project Genome, an ambitious initiative to capture and synthesize the world's supply chain knowledge into actionable intelligence. This is a production engineering role first. You'll build robust Python systems that happen to train and serve LLMs, not the other way around. We need someone who writes production-quality code, debugs complex distributed systems, and thinks about reliability, who has learned ML/LLMs as powerful tools in their engineering arsenal.
You'll work across our entire AI stack:
- Building FastAPI services that serve models
- Creating training pipelines that process production data
- Deploying inference endpoints with proper monitoring
- Integrating all of this into our existing Python backend
The ML is important, but the engineering discipline is what makes it production-ready.
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: Improbable Ltd.
At Kallikor, we pride ourselves on fostering a dynamic and innovative work environment where AI/ML Engineers can thrive. Our commitment to employee growth is evident through continuous learning opportunities and collaborative projects that push the boundaries of supply chain intelligence. Located in a vibrant tech hub, we offer competitive benefits and a culture that values creativity and teamwork, making us an exceptional employer for those seeking meaningful and impactful work.
StudySmarter Expert Advice🤫
We think this is how you could land AI/ML Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with Kallikor folks on LinkedIn. Building relationships can open doors that job applications alone can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI/ML. Having tangible examples of your work can really impress hiring managers and set you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding ML concepts. Practice common algorithms and system design questions, as you'll need to demonstrate your engineering discipline and problem-solving abilities.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in being part of the Kallikor team and contributing to our mission.
We think you need these skills to ace AI/ML Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the AI/ML Engineer role. Highlight your experience with Python, ML models, and any relevant projects that showcase your engineering skills. We want to see how you can contribute to our mission at Kallikor!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for AI and supply chain intelligence, and explain why you're excited about the opportunity at Kallikor. Let us know how your skills align with our goals.
Showcase Your Projects:If you've worked on any relevant projects, make sure to include them in your application. Whether it's building FastAPI services or deploying ML models, we want to see what you've done and how it relates to the role.
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team at Kallikor!
How to prepare for a job interview at Improbable Ltd.
✨Know Your Tech Stack
Familiarise yourself with the technologies mentioned in the job description, like FastAPI and Python. Be ready to discuss how you've used these tools in past projects, especially in building production systems.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific challenges you've faced in previous roles, particularly around debugging complex distributed systems. Use examples that highlight your engineering discipline and how you ensured reliability in your solutions.
✨Understand AI/ML Fundamentals
Brush up on your knowledge of machine learning and large language models. Be prepared to explain how you've applied these concepts in real-world scenarios, especially in relation to supply chain intelligence or similar fields.
✨Ask Insightful Questions
Prepare thoughtful questions about Kallikor's projects, like Project Genome. This shows your genuine interest in their mission and helps you understand how you can contribute to their goals effectively.
