- Maintain and optimize existing MLOps pipelines for ingesting, chunking, embedding, and enriching unstructured data.
- Improve chunking and parsing logic to boost retrieval accuracy and LLM response quality.
- Enhance semantic and vector search performance using OpenSearch and related tools.
- Collaborate closely with Data Scientists, LLM engineers, and Backend Developers.
- Monitor and evolve a cloud-native production system hosted on AWS.
- Ensure the system is secure, scalable, and reliable as usage increases.
- 5+ years in MLOps, ML Engineering, or Data Engineering roles.
- Strong Python skills and hands-on experience with unstructured data.
- Proficiency with chunking strategies, embedding generation, and RAG optimization.
- Experience with OpenSearch, especially semantic/vector search capabilities.
- Familiarity with AWS services: S3, Lambda, Bedrock, SageMaker, CloudWatch.
- Knowledge of containers (Docker, Kubernetes), CI/CD, and IaC tools (Terraform, Ansible, CloudFormation).
- Solid grasp of ML concepts and DevOps best practices.
- Strong communication and troubleshooting skills. English B2+.
Nice to have:
- Experience deploying and scaling LLMs in production.
- Familiarity with LangChain, HuggingFace, FAISS.
- Background in NLP, information retrieval, or knowledge graphs.
- Participation in interesting and demanding projects
- Flexible working hours
- A great, non-corporate atmosphere
- Stable employment conditions (contract of employment or B2B contract)
- Opportunities for development and promotion
- Attractive package of benefits
- Remote work
We reserve the right to contact the selected candidates.