Senior AI/ML Engineer

At Kallikor, we are 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 are 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 will be instrumental in building our first domain-specific language model (DSLM) and the foundation for Project Genome, an ambitious initiative to capture and synthesise the world’s supply chain knowledge into actionable intelligence.

This is a production engineering role first. You will 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 will 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, and 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 will 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 will 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. Hit our 200ms latency targets through engineering, not just throwing bigger GPUs at it.
• Shape Project Genome's foundation: Work with our Principal Engineer to architect how we ingest, process, and learn from global supply chain data. This is systems design as much as ML with data pipelines, graph databases, incremental learning strategies being just as important.
• Mentor through code review and pairing: Raise the bar on code quality, testing, and production practices across the team. Teach mid and junior engineers how to build ML systems that don’t fall over.

What We’re Looking For Specifically

• 5+ years building production Python systems (backend services, APIs, data processing)
• Strong software engineering fundamentals: design patterns, testing, debugging, profiling
• Experience integrating LLMs into applications (OpenAI/Anthropic APIs, prompt engineering, streaming, PydanticAI)
• Understanding of ML training workflows (even if you’re not an expert. You need to know enough to build the infrastructure)
• Docker, CI/CD, production deployment experience
• Can read and understand PyTorch code (you don’t need to write novel architectures)

About Us

Kallikor is determined to foster an environment where people can do their best work and feel like they belong. We believe a healthy culture, strong values and contribution from a diverse range of individuals will help us to achieve success.

We do not discriminate based on race, ethnicity, gender, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.