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описание
The Social Health Insurance Fund finances healthcare services under Kazakhstan’s Guaranteed Benefits Package and Mandatory Social Health Insurance system. It is expanding its digital services, analytics capabilities, and artificial intelligence initiatives, and is building an Artificial Intelligence function and secure AI solutions to support planning, control processes, and healthcare delivery. Its initiatives include internal AI assistants and retrieval-augmented generation (RAG) services.
задачи
Design and maintain secure infrastructure for ML and LLM products, including access controls, audit logging, secrets management, and safeguards for personal and medical data
Build end-to-end MLOps pipelines for data preparation, training, validation, deployment, monitoring, and retraining
Deploy and optimize LLM services, including local models, inference servers, APIs, GPU infrastructure, and performance monitoring
Develop RAG solutions for internal documents, regulations, and knowledge bases; manage vector databases, document indexing, search, and data refresh processes
Establish CI/CD workflows for models, prompts, agentic workflows, and AI services
Monitor model quality, latency, errors, inference costs, and compute utilization
Collaborate with Data Scientists, ML Engineers, Data Engineers, analysts, and business teams to deliver AI use cases
Contribute to technical stack selection and define MLOps/LLMOps standards
требования
2–3+ Years of experience in MLOps, LLMOps, DevOps, ML Engineering, or Data Engineering
Strong Python skills and experience building APIs with FastAPI or similar frameworks
Hands-on experience with Docker, Kubernetes, Git, and CI/CD
Solid understanding of the ML lifecycle, including data preparation, training, evaluation, deployment, monitoring, and retraining
Experience with Airflow, MLflow, S3/MinIO-compatible object storage, PostgreSQL, and/or ClickHouse
Practical knowledge of Hugging Face, Transformers, vLLM, RAG, embeddings, and vector databases; experience with one or more of Qdrant, pgvector, Milvus, Weaviate, Elasticsearch, or OpenSearch
Understanding of agentic workflows, tool calling, LLM orchestration, and LLM evaluation
Experience with observability tools such as Prometheus, Grafana, OpenTelemetry, ELK/EFK, Langfuse, LangSmith, or similar platforms
Understanding of IAM, secrets management, security logging, and audit requirements
Ability to design reliable systems, document technical decisions, and communicate effectively with non-technical stakeholders
Будет плюсом: experience serving models on GPU infrastructure; Terraform, Helm, Argo CD, GitLab CI, or GitHub Actions; AWS, Azure, GCP, private cloud, or on-premises environments; finance, government, healthcare, insurance, or other regulated industries; OCR, document classification, entity extraction, semantic search, or RAG; fraud detection, forecasting, anomaly detection, or risk-scoring models
