AI/ML lead (on-site, no remote, Astana)

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

II + 2- two=?

Responsibilities:

- 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

Requirements:

- 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

Nice to have:

- 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