You will work on scalable AI solutions, developer-focused tools, workflow automation, and AI capabilities that improve engineering productivity and deliver real business value for clients.

Responsibilities:

— Design, build, and deploy LLM-based applications and agentic AI systems from prototype to production.

— Develop production-grade AI agents, multi-agent systems, and tool-using workflows.

— Implement RAG pipelines over proprietary and third-party data sources.

— Build and maintain vector databases for AI data storage, retrieval, and search.

— Design and implement AI-powered capabilities such as code assistance, documentation generation, test automation, summarization, knowledge discovery, and code review support.

— Evaluate foundation models such as OpenAI, Anthropic, Gemini, Llama, and other commercial or open-weight models.

— Build LLM evaluation and benchmarking pipelines, including offline tests, online monitoring, human-in-the-loop review, and quality measurement.

— Improve model performance across relevance, faithfulness, latency, throughput, reliability, and cost.

— Design and maintain APIs, backend services, integrations, data flows, and custom tools for AI systems.

— Build custom tools, skills, integrations, and MCP-style server/client patterns for agentic workflows.

— Deploy, monitor, and optimize AI systems on cloud platforms, with a focus on scalability, reliability, observability, and cost efficiency.

Requirements:

— 2+ years of hands-on experience building and deploying LLM-based applications, RAG systems, or AI agents in production.

— Strong Python experience, ideally 3+ years.

— Experience building backend systems, APIs, services, integrations, and data flows.

— Experience with object-oriented programming, backend architecture, design patterns, and production system design.

— Hands-on experience with LangChain, LangGraph, or similar AI orchestration frameworks.

— Strong experience designing agentic AI systems, tool-using agents, or multi-step AI workflows.

— Experience with RAG architecture, including chunking strategies, embeddings, vector databases, reranking, and retrieval evaluation.

— Experience with vector databases such as Pinecone, Weaviate, Qdrant, Chroma, Milvus, or similar.

— Experience with LLM evaluation and benchmarking, including designing evaluation pipelines and measuring model performance.

— Strong prompt design, prompt iteration, and prompt optimization skills.

— Experience working with major model providers or open-weight models such as OpenAI, Anthropic, Gemini, Llama, or similar.

— Cloud experience with AWS, GCP Vertex AI, Azure AI Foundry, or similar platforms.

— Familiarity with AI safety, responsible AI, guardrails, and output validation for production systems.

— Experience with CI/CD pipelines, deployment workflows, and engineering best practices.

We offer:

— remote time job, B2B contract
— 12 sick leaves and 18 paid vacation business days per year
— Comfortable work conditions (including MacBook Pro and Dell monitor on each workplace)
— Smart environment
— Flexible work schedule
— Competitive salary according to the qualifications