AI Engineer
Who We’re Looking For
We are looking for an AI Software Engineer to design, build, evaluate, and operate AI-powered capabilities within the Creatio platform.
You will join the AI teams building the agent platform that enables AI agents to safely perform real work in the product: call tools and APIs, read and write data, maintain context, follow permissions, and escalate to humans when needed.
The team also owns the core infrastructure for testing, monitoring, logging, observability, and cost/performance control, ensuring AI agents are reliable, scalable, secure, and production-ready.
This role combines strong software engineering expertise with hands-on AI engineering experience. You will work on enterprise-grade AI solutions, including assistants, copilots, knowledge systems, semantic search, RAG-based capabilities, and agentic workflows.
Responsibilities:
- Design and implement AI-powered product features within the Creatio platform.
- Build assistants, copilots, agentic workflows, knowledge systems, RAG, and semantic search capabilities.
- Integrate LLM providers, tools, enterprise data sources, and MCP-based solutions.
- Develop and maintain backend services using C# and .NET.
- Build evaluation frameworks, quality gates, and observability practices for AI solutions.
- Monitor and improve the quality, latency, cost, reliability, and security of AI capabilities.
- Apply AI coding assistants and engineering agents as part of the daily development workflow.
Requirements:
- Strong experience with C# and .NET.
- Hands-on experience with LLMs, prompt engineering, function calling, and structured outputs.
- Understanding of agent architectures, MCP, RAG, embeddings, vector search, and semantic search.
- Experience with AI evaluation, observability, security, and Responsible AI practices.
- Solid understanding of API design, distributed systems, and software architecture.
- Experience with SQL databases, testing, CI/CD, and cloud-native development.
- DevOps fundamentals, including Docker containerization and basic Kubernetes knowledge.
- Strong knowledge of engineering best practices and production-grade software development.
