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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Forward Deployment Engineer - **Company:** UST Inc - **Location:** Kansas City, MO, United States - **Experience:** Expert - **Salary:** $122,000 - $183,000 - **Contract:** Temporary contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Configuration Management Databases, Continuous Integration, Data Systems, Memory Management, Python (Programming Language), Open Source Technology, Systems Development Life Cycle, Pinecone, Data Ingestion, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, AgentCore, Generative AI, Git, Build Management, Pgvector, LangSmith, Information Technology, Amazon Bedrock, Low Latency, Production Code, CrewAI, Weaviate, AutoGen, Claude, Azure AI, Google Gemini - **Published:** November 7, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/88810034/1 ## About the Role * 10-14 years of total technology experience. * At least 3 years of experience designing and deploying production agentic AI or LLM-based systems. * Strong Python expertise with hands-on experience shipping production-grade code. * Deep hands-on experience in Context Engineering and Prompt Engineering. * Production experience designing and implementing RAG pipelines. * Experience with vector databases such as Pinecone, Weaviate, pgvector, or equivalent. * Hands-on experience building and orchestrating multi-agent systems using Strands Agents, LangGraph, LangChain, CrewAI, AutoGen, or similar frameworks. * Experience deploying and operating agents on Amazon Bedrock AgentCore, including Runtime, Memory, Gateway, and Observability. * Strong experience with agent memory management, including session, semantic, summarization, and long-term memory. * Experience implementing semantic caching, prompt caching, KV-cache optimization, and response caching. * Strong understanding of token cost management, context window optimization, model routing, prompt compression, and cost telemetry. * Experience working with foundation models such as Claude, Amazon Nova, GPT-4o, Llama, and Gemini and understanding capability, latency, and cost trade-offs. * Experience with agent observability tools such as LangSmith, Arize, AgentCore Observability, Helicone, or equivalent. * Experience with MCP server design and integration patterns. * Experience with AWS, Azure, or GCP and containerized agent deployment patterns. * Proven technical leadership experience on customer-facing AI delivery engagements. * Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience. Preferred * Experience leading short-cycle, fixed-scope AI delivery engagements with measurable production outcomes and defined exit criteria. * Familiarity with AI governance frameworks, Responsible AI tooling, and enterprise AI usage policy design. * Familiarity with SOC 2 and FedRAMP as they apply to AI system deployments. * Contributions to the AI engineering community through open-source agent projects, technical writing, or conference speaking. * Relevant cloud or AI/ML certifications such as AWS ML Specialty, Azure AI Engineer, or Google Professional ML Engineer. ## Description We are seeking a Principal Forward Deployment Engineer to join our UST-PACE SEALS (Solution Engineering and Architecture Leadership) organization. In this role, you will lead a small, forward-deployed AI squad embedded directly at enterprise client sites. You will personally design, build, and ship production-grade agentic AI systems, while owning engagements from the first sprint through signed closeout. You will architect context engineering frameworks, design RAG pipelines, build and orchestrate multi-agent systems, and deploy them to production runtimes including Amazon Bedrock AgentCore. You will establish KPI baselines, drive delivery against committed outcomes, and leave clients with working agents in production and the capability to scale independently. The ideal candidate is a hands-on engineering leader who writes Python every day, has shipped agentic systems against real enterprise data, and can operate decisively within complex enterprise environments. What You'll Do * Lead and personally contribute to the design and delivery of production agentic AI systems across the full AI-Native SDLC. * Own engagement governance, including sprint cadence, scope control, KPI baseline definition, and progress against committed outcomes. * Architect context engineering frameworks, including system prompts, domain knowledge bases, agent instruction sets, and structured context documents. * Design and implement end-to-end RAG pipelines, including data ingestion, chunking strategies, embedding model selection, vector store configuration, and retrieval quality evaluation. * Build and orchestrate multi-agent systems using Strands Agents, LangGraph, CrewAI, AutoGen, or similar frameworks. * Deploy and operate agents on Amazon Bedrock AgentCore, including Runtime, Memory, Gateway, and Observability services. * Design agent memory architectures covering short-term session memory, long-term persistent memory, and semantic memory stores. * Implement semantic caching, prompt caching, response caching, and KV-cache optimization to improve latency and control inference costs. * Manage token costs through model selection, context window optimization, prompt compression, cost telemetry, and benchmarking. * Select foundation models based on capability, latency, cost, and compliance requirements across Claude, Amazon Nova, GPT-4o, Llama, and Gemini. * Implement end-to-end agent observability, including traces, spans, latency, token usage, error rates, and decision audit logs. * Design and build MCP servers connecting agents to enterprise APIs, data systems, ITSM, CMDB, Git, CI/CD, and observability platforms. * Serve as the technical point of contact for VP-level client sponsors and translate engineering realities into clear business value. * Own engagement closeout, including scope delivered, KPI measurement, artifact handover, and client sponsor sign-off. * Mentor FDE Builders and contribute reusable prompts, agent configurations, memory patterns, and cost benchmarks back into the AI Engineering practice. This position description identifies the responsibilities and tasks typically associated with the performance of the position. Other relevant essential functions may be required., * Production Engineering Rigor: Treats observability, cost management, and memory design as first-class engineering concerns. * Player-Conductor Mindset: Leads by doing and ships production code alongside the team every sprint. * Client Independence: Engineers the client's ability to operate and scale independently from day one. * Executive Communication: Translates engineering complexity into clear business narratives without losing technical precision. * Delivery Focus: Thrives under fixed-scope, time-boxed delivery pressure. * Continuous Innovation: Experiments with emerging agent frameworks, model families, and cost optimization techniques. * Cross-Functional Leadership: Builds trust with both VP-level sponsors and engineering teams.