Senior Software Engineer, Agentic AI

Autodesk•Washington, DC

1d•$117,000 - $209,330•Hybrid

About The Position

We are seeking an experienced Senior Software Engineer to design, build, and operate resilient, scalable, high-performing services for the Product Access platform, which supports the access experience for millions of Autodesk users worldwide. You will own end-to-end solution design and delivery across the full engineering lifecycle and use spec-driven development and agentic systems to build, operate, and support Product Access in production, including agent-based automation for engineering and operational workflows. This role includes shared production on-call responsibilities as part of end-to-end service ownership. You will report to the Senior Engineering Manager and collaborate closely with Product Managers, Architects, Domain Leads, and cross-functional stakeholders across Product Access and beyond.

Requirements

Bachelor's degree or higher in Computer Science, Engineering, or a related field, or equivalent practical experience
6+ years of progressive software engineering experience building and operating Java microservices and serverless architectures in production
Strong Java and Spring Boot skills; solid unit, integration, and system testing; strong debugging in local and integrated environments
Hands-on experience building or integrating AI-powered systems: agents, LLM-based applications, RAG, or tool-calling workflows
Experience with spec-driven development (SDD)
Experience with event-driven streaming (Kinesis, Kafka, or equivalent), containers on AWS (Docker, ECS Fargate), CI/CD (Jenkins, GitHub Actions), and core AWS services (Lambda, DynamoDB, messaging, API gateways, logging, monitoring)
Strong collaboration and communication skills; effective in small teams with shared end-to-end ownership of design, delivery, and production support

Nice To Haves

Proficiency in Python and FastAPI; experience building MCP servers or equivalent tool-use frameworks for AI agents
Experience with RAG, proactive or event-driven agents, and applying agentic AI to engineering workflows, operational automation, or DevOps
Experience designing LLM evaluation frameworks, multi-agent patterns, and human-in-the-loop review gates
Familiarity with high-volume event pipelines (Kinesis/Kafka streaming, REST publish, SNS/SQS/DLQ patterns), Gradle or Maven, OpenTelemetry, and AI tracing tools (Opik, LangSmith, or equivalent)
Understanding of policy-based access control (OPA), authentication for AI systems (OAuth, Entra ID), and Responsible AI practices

Responsibilities

Design, develop, and operate resilient, scalable, high-performing cloud-native Java microservices and distributed systems on AWS for the Product Access platform
Own business features and platform initiatives end to end, from requirement analysis through production support, working with product and engineering partners on design, testing, and operational readiness
Enhance system resiliency, reliability, scalability, and security; optimize performance, troubleshoot production issues, and implement engineering best practices across development and delivery pipelines
Lead project delivery by organizing scope, estimating effort, establishing timelines, and driving medium-to-large initiatives independently
Apply spec-driven development in platform and feature delivery
Build and extend agentic systems and automation: composable skills, MCP integrations, and knowledge retrieval grounded in runbooks, tickets, and service documentation
Deliver proactive triage and write-action automation with human-in-the-loop gates where required, progressing toward autonomous design-to-delivery workflows
Enforce production standards for agentic systems through evaluation, CI quality gates, and end-to-end observability across agents and services
Provide production support; lead incident response and root cause analysis; participate in a shared on-call rotation for production systems, including support outside standard business hours per team schedule; mentor engineers and uphold sound engineering practices