🕒 yesterday
Build and scale production AI tools, agents, integrations, and automation that help teams work more efficiently, while establishing secure, reliable infrastructure for AI adoption across the organization.
AI Engineer
📅 Jun 10
AI EngineeringLLM Prompt EngineeringAgent ArchitecturesMCP
📜 Description
- Build AI product features such as assistants, copilots, agentic workflows, knowledge systems, RAG, and semantic search.
- Design agent architectures covering tools, memory, orchestration, permissions, guardrails, and human escalation.
- Integrate LLM providers, MCP tools, enterprise data sources, embedding pipelines, and vector or hybrid search.
- Create evaluation datasets, quality gates, tracing, logging, and drift detection for AI capabilities.
- Monitor and improve production AI quality, latency, cost, reliability, and security; mitigate AI-specific risks.
- Develop backend services in C# and .NET, using AI coding agents and reviewing their output.
🛠️ Requirements
- Hands-on experience shipping LLM-based features to production: prompt engineering, function/tool calling, structured outputs, context-window and cost management.
- Practical understanding of agent architectures (tool loops, planner/executor, multi-agent, human-in-the-loop), MCP, and at least one agent framework or SDK.
- Working knowledge of RAG: chunking, embeddings, vector and hybrid search, re-ranking, grounding and citations, and common failure modes.
- Experience with AI evaluation (datasets, offline evals, LLM-as-judge), LLM observability and tracing, and AI security (prompt injection, OWASP Top 10 for LLM Applications, Responsible AI practices).
- Daily proficiency with AI coding agents: the ability to direct, review, and verify agent output rather than only write code by hand.
- Solid experience with C# and .NET, API design, distributed systems, and software architecture.
- Experience with SQL databases, automated testing, CI/CD, and cloud-native development; Docker and basic Kubernetes.
- Strong knowledge of engineering best practices and production-grade software development.
- Python for prototyping, data work, and evaluation scripting.
- Hands-on experience with a major AI platform (Anthropic API, Azure OpenAI, AWS Bedrock, Google Vertex AI).
✨ Benefits
- Clear career paths and mentorship opportunities.
- Access to continuous learning.
- Flexible work arrangements and well-being initiatives.
- Recognition of achievements and contributions.
- Competitive compensation and benefits supporting employees and their families.
Full job description
Creatio is an AI CRM and workflow platform where people and AI agents work together — with no limits on users, agents, or scale. We help midsize and large organizations run customer workflows in the AI era. With a global team and a large ecosystem of partners, Creatio serves thousands of customers in over 100 countries and automates tens of millions of workflows daily. Genuine care for our clients and partners is at the heart of our DNA.
We’re proud to be recognized by top industry analysts as a Leader and Strong Performer in multiple Gartner and Forrester reports.
In 2025, Creatio was named to Inc.’s Best Workplaces list, recognizing our commitment to employee wellbeing and a strong workplace culture.
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:
Requirements:
Must have:
Nice to have:
What to expect from us:
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