Senior Ai Engineer

## About the role At our startup, we're not just building AI features — we're rethinking how software behaves through agents that perceive, plan, and act in enterprise environments. As one of our early AI engineers, you'll: - Design and deploy cutting-edge AI agents using our orchestration layer built on n8n, Temporal, and next-gen agentic stacks. - Build intelligent, context-aware workflows that plug directly into enterprise systems — transforming how business make decisions, serve customers, and manage operations. - Prototype fast. Iterate faster. You'll test, fail, learn, and ship production-grade AI agent flows that power real business outcomes. - Collaborate with product and design to identify high-impact user journeys where AI can be a 10x unlock — and then build them. - Push the limits of RAG, embeddings, and vector search to bring real-time, grounded intelligence into every agent decision. - Engineer smart integrations with OpenAI, Anthropic, Claude, and other LLMs — blending in enterprise APIs, CRMs, internal tools, and more. - Lead development of multi-agent orchestration flows: planner-executor models, memory layers, persona-based behaviors, and tool-using agents. - Own the AI pipeline end-to-end: prompt engineering, dynamic context handling, agent simulation and evaluation, all the way to deployment and monitoring. - Help set the bar for reliability, latency, and safety of AI outputs — your work will shape how users trust AI inside their organizations. - Work alongside curious, driven people who care deeply about shipping transformative products, not just academic demos. ## What You Bring - Bachelor's degree in Computer Science or equivalent practical experience. - 8+ years of hands-on experience in applied machine learning, NLP, or AI system development, particularly with LLMs and agentic frameworks. - Deep familairity with large language models such as GPT-4, Claude, or similar; including prompt tuning, fine-tuning, or API-based deployment. - Hands-on experience with Retrieval-Augmented Generation (RAG) pipelines using vector stores and embeddings. - Knowledge of multi-agent systems, task orchestration, and related architectures. - Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow). - Experience integrating with agent orchestration frameworks like LangChain, LangGraph, Temporal, n8n, or custom stacks. - Solid understanding of enterprise software principles, including version control, CI/CD pipelines, API design, and performance monitoring. - Excellent communication and collaboration skills; ability to thrive in a fast-paced, startup environment with evolving priorities.