Job Details

IMS is looking for a Data Science / ML Engineer for a remote position. Responsibilities include developing the AI product contour, working with LLMs, embeddings, and optimizing inference. Requires 2-5 years of experience in Python, ML/DS services, and LLM/NLP.

• Develop the AI product contour: from processing user requests to final response and result visualization. • Design and improve retrieval/pipeline logic for multi-sources (Qdrant, MemGraph, PostgreSQL, non-relational sources). • Develop the LLM-agent contour (Query Planner loop): request classification, subtask decomposition, tool-calls, data sufficiency assessment. • Work with embeddings, semantic search, and output quality (relevance, deduplication, filtering, ranking). • Participate in LLM inference optimization (Qwen family, LiteLLM proxy), request costs, and latency. • Configure and improve data/ML pipelines for document processing, multimodal content, and data preparation for search/graph. • Interact with the backend team (FastAPI/WebSocket), participate in designing contracts and service integrations. • Participate in forming technical solutions for scaling and performance (Redis caching, load, stability).

• 2–5 years of experience in Data Science / ML Engineering (Middle / Middle+ / Senior). • Proficient Python and experience in production development of ML/DS services. • Practical experience with LLM/NLP: prompting, call orchestration, response quality assessment, pipeline optimization. • Experience with vector search and embeddings (preferably Qdrant or similar Vector DB). • Understanding of working with graph and relational DBs (MemGraph/Neo4j, PostgreSQL) in the context of AI search and analytics. • Experience designing APIs/integrations (REST; understanding of WebSocket and service interaction). • Ability to formulate hypotheses, conduct experiments, and make informed technical decisions. Would be a big plus: • Experience building RAG/agentic systems in production. • Experience optimizing LLM models and inference infrastructure (latency/cost/quality trade-off). • Knowledge of Redis caching and session-aware caching approaches. • Experience with multimodal scenarios (text + documents/media). • Experience formalizing service contracts (including gRPC/YAML/OpenAPI). • Experience building data processing and classification pipelines.

• Work on a technologically complex product at the intersection of LLM, search, and graph data. • Opportunity to influence the AI part architecture and key technical decisions. • Flexible remote work format. • Competitive salary based on level. • Strong team, minimal bureaucracy, and live communication.