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# Staff Software Engineer, AI Agent Platform & Runtime

- **Company:** Burges Property Company
- **Location:** New York, NY, United States
- **Salary:** $170,000.0 - $297,200.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Business Logic, Software as a Service, Persistent Data Structure, Distributed Systems, Fault Tolerance, Python (Programming Language), Key Management, Machine Learning, Regression Testing, Secure Coding, TypeScript, Cloud Platform System, Large Language Models, Multi-Agent Systems, Prompt Engineering, Caching, Backend, Kotlin, Event Driven Architecture, Kubernetes, Low Latency, Virtual Agents, Docker, Golang
- **Published:** October 27, 2026
- **Apply:** https://www.dice.com/job-detail/e683ae4a-7d16-4fb9-ba6b-b25a4f741ff3

## About the Role

* Experienced in building and operating production software, with a track record of leading complex technical initiatives across teams.
 * Deep expertise in backend platforms, distributed systems, asynchronous execution, APIs, fault tolerance, and stateful services.
 * Strong hands-on Python proficiency and experience with a modern language such as TypeScript, Java, Go, Kotlin, or Rust.
 * Experience developing production LLM applications involving tool calling, multi-step workflows, retrieval, agent orchestration, or context management.
 * Practical understanding of containers, cloud-native infrastructure, workload isolation, and secure execution patterns.
 * Strong foundations in data persistence, caching, event-driven architectures, permissions, multi-tenancy, and secrets management.
 * Excellent product and architectural judgment: able to evaluate emerging techniques, balance speed with reliability, and turn ambiguous problems into maintainable platforms.
 * Ability to influence technical strategy, mentor senior engineers, and communicate complex tradeoffs clearly to engineering and product leaders.

Nice to Have

 * Custom agent runtimes or orchestration frameworks; agentic coding systems or autonomous development environments.

 * Persistent or hierarchical agent memory, context engineering, retrieval architectures, or long-running agent workflows.

 * Secure code execution, sandboxing, Docker, Kubernetes, microVMs, or ephemeral compute.

 * Multi-agent coordination, MCP servers, tool registries, capability-based permissions, model routing, or agent evaluation and tracing.

 * Enterprise SaaS, M&A, virtual data rooms, regulated data, financial services, or complex document-centric workflows.

## Description

* Lead the architecture and evolution of Datasite's custom agent harness, including reasoning and execution loops, planning, tool selection, state transitions, structured outputs, and runtime controls.

 * Design layered agent memory spanning working context, episodic history, semantic knowledge, and durable workflow state; build retrieval, consolidation, compaction, provenance, retention, and access controls.

 * Architect isolated, containerized agent execution environments for code, files, and tools, with ephemeral workloads, tenant boundaries, resource quotas, restricted networking, and secure credential handling.

 * Build resilient agent orchestration: asynchronous tasks, specialized subagents, delegation, checkpoints, resumability, retries, failure recovery, and human approvals.

 * Create secure, extensible tool integrations using MCP and related interfaces, with authentication, authorization, least-privilege permissions, and safeguards against prompt injection and unsafe actions.

 * Establish agent evaluation, tracing, observability, and regression testing to measure task quality, correctness, reliability, latency, and cost; drive measurable improvements
 * Make high-impact technical tradeoffs between probabilistic model behavior and deterministic application logic, validation, or human review.
 * Partner with Product, Machine Learning, Security, and platform teams to turn diligence, research, and deal workflows into production-grade agent experiences; mentor engineers while remaining hands-on in critical implementation.