AI Agent Development Engineer (Customer Systems)

Quick Facts

• Role: AI Agent Development Engineer (Customer Systems)

Description

Build autonomous AI agents that leverage large language models to reason, plan, use tools, and execute multi-step actions to achieve defined goals. Own responsibilities across architecture, agent development, tool integration, memory and context management, prompt engineering, evaluation and safety, and production deployment at scale.

Benefits

• Opportunity to work on next-generation intelligent automation that improves customer support experiences.

• Build and manage systems, frameworks, and applications used by customers and employees daily.

• Work in an environment focused on continuous testing, monitoring, and adaptation for non-deterministic AI systems.

Responsibilities

• Design foundational AI agent architecture, including reasoning and decision-making workflows.

• Build agents using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI.

• Integrate agents with external tools, databases, APIs, and business systems.

• Implement short- and long-term memory and context management (e.g., vector databases, RAG).

• Create domain-specific prompts and prompt workflows for improved reasoning and performance.

• Implement human-in-the-loop processes, evaluation frameworks (evals), and safety guardrails.

• Deploy and scale agents in production using Docker, Kubernetes, and cloud platforms.

Requirements

• B.S. in Computer Science, Computer Engineering, or related technical field, or equivalent professional work experience.

• 7+ years proven experience developing software professionally (longer experience preferred).

• 2+ years of professional software development with a focus on backend systems or AI/ML applications.

• Strong proficiency in TypeScript for robust, scalable applications.

• Experience with at least one major AI agent framework: LangChain, AutoGen, LangGraph, or Semantic Kernel.

• Deep understanding of large language models (LLMs), including capabilities and limitations, with examples such as GPT-4o, Claude 3.5 Sonnet, or Gemini.

• Practical experience with Retrieval-Augmented Generation (RAG) and fine-tuning techniques.

• Experience with vector databases (Pinecone, Milvus, Qdrant) and SQL/NoSQL databases.

• Proficiency with cloud platforms (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes) for deployment and management.

Preferred Qualifications

• Experience in AI/ML or automation systems beyond basic scripting.

• Background as an ML Engineer or Full-Stack Developer with interest in autonomous systems.

• Familiarity with JavaScript/TypeScript for agent integration and tooling.

• Experience designing multi-agent systems or agentic workflows.

• Understanding of ethical AI principles, bias detection, and mitigation.

• Contributions to open-source AI agent frameworks or relevant research.

• Ability to translate complex business problems into agent-based solutions.