AI Agent Engineer

AI Agent Engineer

Enterprise AI Agents and Data Science Agents

agentic AI meets production engineering

Responsibilities

• Design, build, and own Enterprise AI Agent and AI Data Science Agent systems that combine: reasoning and planning; tool orchestration; memory and state management
• Lead the development of scalable agent architectures tailored for enterprise and regulated environments.
• Build agent capabilities for: automated analysis and reporting; data wrangling and transformation; domain-specific workflows across business and industry verticals
• Integrate real-time and enterprise-grade systems into agent workflows: RAG pipelines; enterprise APIs and internal tools; proprietary data sources and knowledge bases
• Collaborate closely with research, alignment, and explainability teams to ensure: agent behavior is safe, auditable, and interpretable; reasoning paths and decisions can be inspected and explained
• Continuously evaluate and incorporate advances in agentic and generative AI: controllability techniques; multi-modal agents; memory architectures and long-horizon planning
• Contribute to Lexsi’s engineering culture through: design discussions, code reviews, setting a high bar for technical quality and ownership
  

Requirements

• Demonstrated experience building systems using LLMs and agentic frameworks, such as: LangChain, LangGraph, LangFlow, Semantic Kernel, Haystack; ReAct-style agents, AutoGPT-like systems, or custom agent frameworks
• Experience productionizing AI/LLM workflows and integrating them into real-world applications.
• 2+ years of professional software engineering experience, ideally including: early-stage startups, or AI-first / ML-heavy environments
• Strong proficiency in Python and solid understanding of backend systems: APIs and services; cloud infrastructure; databases and data pipelines
• Strong systems-thinking mindset: you understand trade-offs between performance, reliability, cost, and safety.
  

ethical, explainable, and high-impact AI systems