Senior Software Engineer, Agentic Platform - Moveworks

Company Description Who we are Moveworks is the Agentic AI Assistant platform that empowers the entire workforce. Our platform enables employees to converse with all of their business systems through natural language to quickly find answers and automate tasks. Powered by the world's most advanced LLMs, our proprietary models, and a sophisticated Agentic AI platform, we're transforming how work gets done by allowing AI to take initiative, streamline complex workflows, and continuously learn and adapt. Moveworks is trusted by over 5.5 million employees at more than 350 of the world’s largest companies, including 10% of the Fortune 500, to automate everyday tasks and streamline business operations. Recognized on the Forbes Cloud 100 and AI 50 lists, Moveworks was also named one of Fast Company’s 2025 Most Innovative Companies and Inc’s Best in Business, in the Best in Innovation category. Moveworks was also recognized at Microsoft’s 2025 Partner of the Year and in 2024, received the AI Breakthrough Award. In December 2025, Moveworks was acquired by ServiceNow, marking a pivotal milestone in our journey to create a single front door to work for all business systems. By combining ServiceNow’s leading workflow automation with Moveworks’ Reasoning Engine and natural language capabilities, we deliver the AI platform for every person and every workflow. Built to go beyond basic summaries to deliver meaningful business impact. Together, our AI acts across enterprise systems to turn conversations into completed work. By joining our team, you’ll be at the forefront of the AI transformation, backed by the global scale of ServiceNow and the agility of a high-growth company. We are looking for world-class talent to help us extend agentic AI to every employee across every corner of the business. Come join us! ServiceNow It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

Job Description

The Role We're building the runtime infrastructure that powers Moveworks' AI agents — the systems that orchestrate, execute, and deliver agent responses to millions of enterprise users in real time. This role sits where large language models meet distributed systems. You'll apply machine learning, LLM, and distributed systems expertise to make agents plan, reason, and use tools reliably, and you'll build the production systems that run those agents at enterprise scale. Our AI agents can plan, execute multi-step workflows, call tools, wait on human input, and resume — all while maintaining correctness, observability, and low latency. The systems that make this possible are what you'll build and own. What you get to do in this role:

• Agent orchestration engine — A state machine that manages long-running agent sessions, coordinating planning, execution, and user interaction across multiple LLM calls and tool invocations
• Model routing and adaptation — Choose, route between, and fine-tune or distill LLMs for specific agent tasks. Meet enterprise requirements such as granular permissions, data freshness, explainability, and preserving general knowledge.
• Distributed session management — Lease-based ownership using conditional writes, heartbeat protocols, and crash recovery via checkpointing
• Event-driven message pipeline — FIFO queues for ordered delivery, consumers for event processing, and real-time streaming via gRPC and [Socket.IO](
• Structured concurrency — Running multiple concurrent tasks per session with fail-fast semantics and graceful cancellation
• Observability infrastructure — OpenTelemetry instrumentation, distributed trace context propagation across async boundaries, custom span lifecycle management for sessions that span minutes
• Caching and state layers — Redis, DynamoDB KV stores with per-org/per-bot scoping, batch read optimization, and hot-reload configuration
• Tool and agent connectivity for LLMs: Build MCP and agent-to-agent (A2A) capabilities, including tool discovery, namespacing, and tool selection over large tool catalogs, so the LLM picks and calls the right tool from thousands of candidates.
• Token efficiency and context engineering — Lower the agent's cost and latency through prompt and context discipline, caching, speculative and parallel model-call scheduling, and reducing tokens spent across multi-step LLM reasoning chains.
• Open-weights model adoption and evaluation — Quickly stand up newly released open-weights LLMs at different quantization levels, run them in a test environment, and benchmark their accuracy, latency and cost on agent tasks. Use the results to decide which models reach production.

Qualifications

To be successful in this role you have: You should have deep experience in at least 3 of these areas:

• Distributed systems: consistency models, idempotency, exactly-once delivery, distributed locking/leasing
• Concurrent/async programming: Python asyncio, Go goroutines, structured concurrency, cancellation handling
• Event-driven architectures: message queues (SQS, Kafka), pub/sub, backpressure, delivery guarantees
• Database systems for infrastructure: DynamoDB (conditional writes, transactions), Redis (connection pooling, pub/sub)
• Observability: OpenTelemetry, distributed tracing, span context propagation, Prometheus metrics
• gRPC/protobuf: streaming RPCs, service interface design, error handling patterns
• Machine learning: Large language models, natural language processing, LLM evaluation

Required:

• 5+ years building production backend/infrastructure systems
• Strong in Python or Go (ideally both)
• Experience designing and operating systems that handle real traffic at scale
• Hands-on experience building production systems that use LLMs or other ML models
• Comfort with ambiguity — these are novel problems without textbook solutions

Relevant past experience:

• Building and shipping products on large language models with techniques such as RAG, chain-of-thought, self-reflection, tool and function calling, and agentic reasoning.
• Training, fine-tuning, or evaluating machine learning models on large datasets in production, especially for NLP, retrieval, or ranking.
• Improving relevance or quality through careful experimentation: designing metrics, building evaluation datasets, and developing new features and signals for ranking or model selection.
• Research or applied work in machine learning, LLMs, agentic systems, information retrieval, or ranking and relevance.

Additional Information

Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. [Learn more here]( To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact [email protected] for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.