Senior AI Agent Engineer (W2 only)

1. Bachelor''s or Master''s degree in Computer Science, Software Engineering, or related field (or equivalent practical experience).

2. 3+ years building production software systems, including 1–2+ years on ML/AI or LLM-based applications.

3. Proven experience designing and deploying multi-agent or multi-service architectures in production — not just notebooks or demos. As one 2026 hiring analysis puts it, the job is closer to distributed systems engineering with a probabilistic component than it is to ML research or prompt tweaking 

4. Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask).

5. Hands-on experience with agent orchestration frameworks — LangGraph, CrewAI, LlamaIndex, or equivalent — for building stateful, multi-step, tool-using agent workflows.

6. Cloud deployment experience, ideally Google Cloud Platform (BigQuery, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services.

7. Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, GitHub Actions/Cloud Build).

• Bachelor''s or Master''s degree in Computer Science, Software Engineering, or related field (or equivalent practical experience).
• 3+ years building production software systems, including 1–2+ years on ML/AI or LLM-based applications.
• Proven experience designing and deploying multi-agent or multi-service architectures in production — not just notebooks or demos. As one 2026 hiring analysis puts it, the job is closer to distributed systems engineering with a probabilistic component than it is to ML research or prompt tweaking .
• Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask).
• Hands-on experience with agent orchestration frameworks — LangGraph, CrewAI, LlamaIndex, or equivalent — for building stateful, multi-step, tool-using agent workflows.
• Practical experience building RAG pipelines: vector databases (pgvector, Pinecone, Weaviate, or Qdrant), embeddings, chunking strategies, and retrieval evaluation.
• Cloud deployment experience, ideally Google Cloud Platform (BigQuery, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services.
• Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, GitHub Actions/Cloud Build).
• Experience building evaluation and observability pipelines for LLM/agent systems — offline eval sets, LLM-as-judge scoring, and tracing tools (LangSmith, Langfuse, OpenTelemetry, or equivalent) to track task success, latency, and cost.
• Understanding of LLM safety practices: guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code/SQL (sandboxing, least privilege).
• Solid software engineering fundamentals: API design, testing, version control, security best practices.