Senior Software Engineer, Agentic AI Systems

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Senior Software Engineer developing agentic systems at Cloudary, a cloud consulting firm. Build and deploy LLM solutions for high-performing tech teams in North America.

Posted 7/22/2026contractRemote • Washington • 🇺🇸 United StatesSenior💰 $45 per hourWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in End-to-End Delivery of LLM applications, including architecture defense, orchestration, and integration of tools and retrieval systems. Proficient in managing production environments on AWS Bedrock and implementing evaluation and observability practices with a focus on cost, latency, and resilience.

Highest-signal resume keywords

End-To-End DeliveryLLM Application EngineeringAgent Orchestration ExperienceProduction AWS ManagementEvaluation & Observability

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonTypeScriptGoPrompt DesignTool/Function CallingContext ManagementToken BudgetingCI/CD PipelinesTerraformOpenTelemetry

Soft Skills

Client CommunicationTechnical Decision-Making

Tools & Technologies

LangGraphLangChainAWS BedrockGoogle Vertex AIAzure AI Foundry

Industry Keywords

Agent OrchestrationModel InvocationDistributed TracingCost AttributionLatency Management

Tech Stack

Tools & technologies

AWSAzureGoPythonTerraformTypeScript

About the role

Key responsibilities & impact

Own End-to-End Delivery: Take a production LLM agent from requirement to production deploy, and defend the architecture and trade-offs directly with the client.
Agent Orchestration: Build and harden orchestration (LangGraph / LangChain or equivalent) — routing, tool-calling, planning, synthesis, and state management.
Tool & Retrieval Integration: Integrate tools over MCP and keep a growing tool surface fast and correct, including BM25, hybrid, or vector retrieval as scale demands.
AWS Bedrock & AgentCore: Run models on Bedrock and Bedrock AgentCore — model selection/routing, guardrails, memory, and regional residency profiles.
Evaluation & Observability: Build the eval harness (golden sets, LLM-as-judge, quality gates wired into CI) and instrument the system with OpenTelemetry for per-session token, cost, and latency attribution.
Cost, Latency & Resilience: Drive down cost and latency with real levers (model routing, prompt caching, payload pruning, parallelizing independent calls) behind a regression gate, and build in circuit breakers, fallbacks, and dead-letter handling.
Safe Write-Actions: Design and stage write-actions with least-privilege permissions, human-in-the-loop approval, plan versioning, audit trail, and rollback — released behind feature flags to a small cohort first.

Requirements

What you’ll need

5+ Years in Software Engineering: At least 2 of them genuinely at a senior level, with strong Python in production and comfort picking up TypeScript or Go when a project calls for it.
Hands-On LLM Application Engineering: Prompt design, tool/function calling, structured output, context management, and token budgeting, in a system real users hit.
Agent Orchestration Experience: Built or operated orchestration with a framework like LangGraph or LangChain, or hand-rolled, beyond single-prompt calls.
Managed LLM/Agent Platform in Production: AWS Bedrock, Google Vertex AI, or Azure AI Foundry — model invocation, streaming, guardrails, and agent tooling. We use Bedrock and Bedrock AgentCore; equivalent depth on Vertex AI or Azure transfers directly.
Evaluation, Retrieval & Observability: Eval harnesses and golden/reference sets, vector or hybrid search in production, and OpenTelemetry-based distributed tracing with token/cost/latency attribution.
Production AWS, CI/CD & IaC: Real IAM, networking, storage, and observability experience, CI/CD pipelines versioned as code, and Terraform in production.
Working English: Comfortable defending system design and technical decisions directly on client calls.

Benefits

Comp & perks

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Own End-to-End Delivery: Take a production LLM agent from requirement to production deploy, and defend the architecture and trade-offs directly with the client.
Agent Orchestration: Build and harden orchestration (LangGraph / LangChain or equivalent) — routing, tool-calling, planning, synthesis, and state management.
Tool & Retrieval Integration: Integrate tools over MCP and keep a growing tool surface fast and correct, including BM25, hybrid, or vector retrieval as scale demands.
AWS Bedrock & AgentCore: Run models on Bedrock and Bedrock AgentCore — model selection/routing, guardrails, memory, and regional residency profiles.
Evaluation & Observability: Build the eval harness (golden sets, LLM-as-judge, quality gates wired into CI) and instrument the system with OpenTelemetry for per-session token, cost, and latency attribution.
Cost, Latency & Resilience: Drive down cost and latency with real levers (model routing, prompt caching, payload pruning, parallelizing independent calls) behind a regression gate, and build in circuit breakers, fallbacks, and dead-letter handling.
Safe Write-Actions: Design and stage write-actions with least-privilege permissions, human-in-the-loop approval, plan versioning, audit trail, and rollback — released behind feature flags to a small cohort first. 🎯 Requirements
5+ Years in Software Engineering: At least 2 of them genuinely at a senior level, with strong Python in production and comfort picking up TypeScript or Go when a project calls for it.
Hands-On LLM Application Engineering: Prompt design, tool/function calling, structured output, context management, and token budgeting, in a system real users hit.
Agent Orchestration Experience: Built or operated orchestration with a framework like LangGraph or LangChain, or hand-rolled, beyond single-prompt calls.
Managed LLM/Agent Platform in Production: AWS Bedrock, Google Vertex AI, or Azure AI Foundry — model invocation, streaming, guardrails, and agent tooling. We use Bedrock and Bedrock AgentCore; equivalent depth on Vertex AI or Azure transfers directly.
Evaluation, Retrieval & Observability: Eval harnesses and golden/reference sets, vector or hybrid search in production, and OpenTelemetry-based distributed tracing with token/cost/latency attribution.
Production AWS, CI/CD & IaC: Real IAM, networking, storage, and observability experience, CI/CD pipelines versioned as code, and Terraform in production.
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