Your Role as Machine Learning Engineer
What makes this role stand out:
- You’re closest to the models and AI itself, building ML/LLM pipelines, integrating, and optimizing models.
- You have a background in deep learning, NLP, or CV, and today you’re hands-on with GenAI and LLMs.
- You know techniques like fine-tuning, prompting, quantization, and LoRA and you understand how models work and how to adapt them for production.
Why it’s worth it:
- You’ll dive into the hottest areas of AI: LLMs, agentic frameworks, RAG, inference optimization, and fine-tuning.
- Projects aren’t just PoCs, the models you build go into production and reach real users.
- You won’t be boxed into “just ML” you’ll collaborate with Data Scientists, Software Engineers, and MLOps to deliver end-to-end solutions.
A few project examples:
- Training multimodal LLMs for drug discovery.
- Building AI voicebots that double conversion rates.
- Creating a GenAI solution for a leading US legal company together with the OpenAI team.
- Running GenAI on edge devices with cloud-level performance.
All of this in a setup that feels like an AI-driven software house: remote-first, flexible, and packed with specialists who are open to sharing knowledge and experimenting with the newest tech.
The ideal candidate:
- Has 4–5+ years of experience in ML engineering and working with models in production environments.
- Brings hands-on expertise with Large Language Models (LLMs) and Generative AI, including integration and inference optimization (latency, cost, scalability).
- Is familiar with frameworks and tools for building and orchestrating LLM pipelines (LangChain, LlamaIndex, RAG, agent frameworks).
- Can design and implement end-to-end ML/LLM pipelines, from data preparation and training/fine-tuning to production-grade APIs.
- Has experience with cloud platforms (AWS, GCP, Azure) and their AI/ML services (e.g., SageMaker, Vertex AI, Azure ML).
- Has worked with SQL, NoSQL, and vector databases (Pinecone, FAISS, Weaviate).
- Is fluent in Python and experienced with ML frameworks (PyTorch, TensorFlow, Hugging Face).
- Knows how to deploy and monitor models (MLOps: CI/CD for models, logging, observability, quality monitoring).
- Communicates clearly and can collaborate effectively with both Data Scientists and product/client teams.
- Bonus: experience in prompt engineering and building simple AI user interfaces (Streamlit, Gradio).