Senior Software Engineer - Agent Development

ServiceNow•Santa Clara, CA

7h•$143,200 - $243,400•Remote

About The Position

This role involves designing and developing scalable, maintainable, and reusable software components with a strong emphasis on performance and reliability. The engineer will collaborate with product managers to translate requirements into well-architected solutions, owning features from design through delivery. Building intuitive and extensible user experiences using modern UI frameworks is key, ensuring flexibility for customer-specific needs. The role also includes contributing to the design and implementation of new products and features while enhancing existing product capabilities, and integrating automated testing into development workflows to ensure consistent quality across releases. Participation in design and code reviews is expected to ensure best practices in performance, maintainability, and testability. Developing comprehensive test strategies covering functional, regression, integration, and performance aspects is also a responsibility. Furthermore, the engineer will mentor other engineers on the team in how to build, maintain, and evolve great AI powered software solutions, and foster a culture of continuous learning and improvement by sharing best practices in engineering and quality. Promoting a culture of engineering craftsmanship, knowledge-sharing, and thoughtful quality practices across the team is also a core aspect of this role.

Requirements

Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
5+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems with a Bachelor's degree; or 3 years and a Master's degree; or a PhD without experience; or equivalent work experience.
Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes.
Production-grade Python.
Working experience with frontier AI SDKs (Anthropic, Google, or OpenAI) — prompt engineering, structured outputs, and model evaluation in production settings.
Familiarity with RAG and retrieval patterns in production — vector stores, hybrid search, and retrieval evaluation metrics.
Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices.
Comfort operating in fast-moving, ambiguous, startup-like AI product environments with high degree of ownership.
Strong appetite for continuous incremental wins and completing challenging deliverables fast.

Nice To Haves

Systems language (Go, Java, or C++) is a plus.
Experience with multi-agent coordination patterns (A2A, MCP).
Familiarity with agent frameworks (LangChain, LlamaIndex, or similar).
Prior experience shipping AI systems in enterprise software.
Experience with AI observability tooling (tracing, cost tracking, LLM-specific monitoring).

Responsibilities

Design and develop scalable, maintainable, and reusable software components with a strong emphasis on performance and reliability.
Collaborate with product managers to translate requirements into well-architected solutions, owning features from design through delivery.
Build intuitive and extensible user experiences using modern UI frameworks, ensuring flexibility for customer-specific needs.
Contribute to the design and implementation of new products and features while enhancing existing product capabilities.
Integrate automated testing into development workflows to ensure consistent quality across releases.
Participate in design and code reviews ensuring best practices in performance, maintainability, and testability.
Develop comprehensive test strategies covering functional, regression, integration, and performance aspects.
Mentor other engineers on the team in how to build, maintain, and evolve great AI powered software solutions.
Foster a culture of continuous learning and improvement by sharing best practices in engineering and quality.
Promote a culture of engineering craftsmanship, knowledge-sharing, and thoughtful quality practices across the team.