ML/AI Lead

Навыки

• AI-агенты
• Airflow
• AWS
• Celery
• Docker
• FastAPI
• Дообучение моделей

Задачи

• Design and build multi-step LLM agents and pipelines for medical data curation
• Develop production RAG systems with high grounding and citation quality
• Deploy and optimize self-hosted LLM inference (vLLM) with cost and throughput optimization
• Fine-tune open-source models on domain-specific medical data with measurable improvement
• Build data pipelines and versioning for documents, embeddings and training data
• Create evaluation frameworks, guardrails and hallucination control systems
• Implement reliable structured output and data extraction
• Work closely with physicians and Platform Architect
• астана #ml
• Company: MDiet (mdiet.org) — early-stage healthtech-стартап

Требования

• Strong hands-on experience with production LLM systems (RAG + agents + inference)
• Experience with fine-tuning open-source LLMs (LoRA/QLoRA/PEFT) with measurable results
• Experience building data pipelines and versioning for LLM/RAG workloads
• Strong skills in evaluation, hallucination reduction and structured output
• Production Python + FastAPI + async processing
• Experience with self-hosted inference (vLLM or similar) and GPU optimization
• English proficiency at C1 level (strong written and spoken communication skills)
• Bachelor’s degree (or higher) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field
• 5+ years in ML/AI, with 1.5–2+ years in production LLM projects
• Experience with MLOps

Будет плюсом

• Experience in medical / clinical NLP or regulated domains
• Cost modeling and optimization of LLM workloads
• MLOps and production fine-tuning pipelines
• Apache Airflow, Prometheus/Grafana, or AI Solution Architecture
• Publications in relevant fields
• Contacts
• Telegram: @team_mdiet
• Email: team@mdiet.org

Условия

• Location: Astana (office 7–9 months → hybrid/remote)
• Salary: $4,000 – $5,000 USD gross + equity
• MDiet is an early-stage HealthTech startup developing an AI-powered platform that helps reduce patient readmissions.The product targets the US healthcare market
• Relocation to the US upon successful company growth (discussed individually)
• Tech Stack
• LangGraph + LangChain (Agentic AI)
• vLLM + Hugging Face
• PyTorch + PEFT (LoRA/QLoRA)
• Vector databases (pgvector, Qdrant, Weaviate, Chroma, Pinecone)
• Advanced RAG + Prompt Engineering + Embeddings
• FastAPI + PostgreSQL
• Docker + Kubernetes + async processing (Arq / Celery / Redis Streams)
• Apache Airflow (data pipelines)
• AWS Cloud (EC2, S3, SageMaker, Lambda, RDS, Bedrock)
• Structured output (Outlines / Guidance / Instructor)
• Evaluation & observability (Langfuse, RAGAS / DeepEval, Prometheus + Grafana)
• MLOps practices

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