AI/ML Engineer

• •**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. 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.

**Must have:**

• •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)

**Nice to have:**

• •Fine-tuning experience (LoRA, full fine-tuning, QLoRA)
• •Distributed training basics (DeepSpeed, FSDP)
• •Graph databases (Memgraph, Neo4j)
• •Supply chain or logistics domain knowledge
• •Experience with agent frameworks (LangChain, PydanticAI, etc.)