Senior Agent Engineer

Job Summary

We are seeking a Senior AI Agent Engineer who is, first and foremost, a strong software engineer with applied Machine Learning expertise. Unlike a pure Data Science role, this position requires deep production engineering skills to build, scale, and evaluate robust AI agentic workflows integrated directly into our core backend. You will leverage LLMs and software best practices to create autonomous systems that are reliable, observable, and production-ready.

Key Responsibilities

• Agent Architecture & Orchestration: Architect end-to-end AI agent systems capable of multi-step reasoning, planning, and self-correction. Design complex control flows (loops, conditionals, DAGs) rather than simple linear chains.
• Production Engineering & Integration: Integrate agents seamlessly into production backends. Build robust integrations enabling agents to use APIs, databases, and vector stores with strict data validation.
• State & Memory Management: Design systems to manage agent context windows, conversation history, and long-term memory (Vector DBs) efficiently to balance performance and cost.
• Reliability & Evaluation (Core Focus): Apply engineering rigor to agent reliability. Design evaluation pipelines (e.g., LLM-as-a-judge, functional unit tests) to quantify performance and handle the non-deterministic nature of LLMs.
• System Scalability & Economics: Optimize agent workflows for latency and token usage costs. Implement caching, retries, and async processing to ensure high throughput.
• Observability & Monitoring: Instrument code to provide deep visibility into agent reasoning steps (tracing), ensuring failures can be debugged in production environments.
• Data Analysis and Reporting: Extract data using SQL and Python and conduct comprehensive data analysis to identify trends, anomalies, and patterns.
  
  

Required Skills And Qualifications

• Education: Bachelor's or Master's in CS, AI, or equivalent practical experience.
• Software Engineering Mastery: Deep expertise in Python, system design, testing, Git, and deploying production-grade services. This is an engineering-heavy role.
• Backend Infrastructure: Proven experience developing and maintaining backend infrastructure (AWS/Azure/GCP) and containerization (Docker/Kubernetes).
• Agentic Frameworks: Hands-on experience with frameworks such as LangChain, LlamaIndex, LangGraph, CrewAI, or AutoGen.
• Applied ML & RAG: Experience with grounding techniques (RAG, vector search), prompt engineering, and utilizing Embeddings APIs.
• Evaluation Rigor: Experience building automated evaluation harnesses to test agent logic against "Golden Datasets."
• Problem-Solving: Excellent analytical skills to debug complex, non-deterministic system failures.
• Team Mentorship: Ability to inspire, motivate, and mentor fellow team members through activities such as code reviews and technical meetings.
  
  

Nice-to-Have

• Familiarity with neural network fundamentals (e.g., Transformers).
• Experience with LLM Observability tools (e.g., LangSmith, Arize Phoenix).
• Understanding of LLM security (e.g., prompt injection defense).