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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Engineer 2, AI Agentic Solutions - **Company:** THE JUDGE GROUP, INC. - **Location:** Seattle, WA, United States (Remote available) - **Experience:** Expert - **Salary:** $166,400.0 - $176,800.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Cloud Engineering, Computer Engineering, Distributed Data Store, Distributed Systems, Memory Management, Python (Programming Language), NoSQL, Open Source Technology, Standard Sql, Search Technologies, Software Engineering, Google Cloud, Large Language Models, Multi-Agent Systems, Generative AI, Backend, Kubernetes, Information Technology, Docker - **Published:** September 29, 2026 - **Apply:** https://www.dice.com/job-detail/1d2c8b3f-b968-496b-8f95-2f8243a95640 ## About the Role * Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience. * 6+ years of professional software engineering experience designing, building, and operating scalable backend services or distributed systems. * Direct production experience building generative AI applications using foundation model APIs (e.g., OpenAI, Anthropic, Gemini), dynamic prompt engineering, and Retrieval-Augmented Generation (RAG). * Proficiency in Python and cloud-native software development on AWS, Google Cloud Platform, or Azure. * Practical experience implementing evaluation metrics, safety guardrails, and telemetry for production LLM systems., * Master's degree in Computer Science, Artificial Intelligence, or a related quantitative field. * Experience deploying agentic orchestration frameworks in production (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, or Claude Agent SDK). * Hands-on experience with multi-agent orchestration architectures, including task decomposition, tool routing, and human-in-the-loop workflows. * Strong background in database engineering and distributed storage across relational SQL, NoSQL, and vector databases (e.g., Pinecone, Milvus, pgvector). * Proven experience with containerization and orchestration tooling (Docker, Kubernetes) alongside automated CI/CD pipelines. * Familiarity with enterprise-scale transactional domains, such as digital commerce, omnichannel retail, inventory supply chain, or customer workflow automation. * Active contributions to open-source AI tooling or peer-reviewed research in applied LLM systems. ## Description As a Software Engineer II on the Enterprise AI team, you will serve as a lead individual contributor driving the technical vision, architecture, and production delivery of autonomous agentic systems. You are a product-minded engineer capable of translating complex, ambiguous business problems into scalable AI-driven solutions spanning multi-month roadmaps. In this role, you will define agent user experiences, establish evaluation benchmarks, optimize token economics, and guide system design across cross-functional engineering, product, and security organizations., * Architect & Deploy Agentic Systems: Design, build, and maintain production-grade agent orchestration pipelines, structured tool-use integrations, and enterprise downstream system connectors. * Context Engineering & Retrieval: Formulate context-window strategies balancing token economics, latency constraints, and response quality across RAG architectures, vector search, and dynamic prompt assembly. * Evaluation, Observability & Guardrails: Build end-to-end evaluation frameworks, offline benchmark suites, automated safety guardrails, and real-time production telemetry to guarantee reliability, safety, and deterministic performance. * State & Memory Management: Implement durable conversation states, dynamic working context, and long-term memory architectures across multi-session user journeys. * Technical Trade-Offs & Cost Governance: Balance rapid delivery against long-term architectural scalability, factoring in foundation model selection, context caching, inference costs, and cloud infrastructure budgets. * Technical Leadership & Quality Bar: Lead rigorous design and code reviews, establish enterprise AI coding standards, mentor junior engineers, and interview prospective technical candidates. * Cross-Functional Collaboration: Partner closely with Product, Security, and Cloud Infrastructure teams to resolve architectural friction and align AI capabilities with corporate roadmaps. ## Related Videos - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Tomorrow's cloud data platforms - fully managed database-as-a-service (DBaaS)](https://www.wearedevelopers.com/videos/254-tomorrow-s-cloud-data-platforms-fully-managed-database-as-a-service-dbaas) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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