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# AI Engineer

- **Company:** Kobie Marketing
- **Location:** Dallas, TX, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, ARM Architecture, JSON, Python (Programming Language), SQL Databases, Data Streaming, Large Language Models, Snowflake, Grafana, Multi-Agent Systems, Git, Machine Learning Operations, Cloudwatch, Api Management, Docker
- **Published:** June 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9d0256d1a4825c51

## About the Role

Do you have experience in Testing and evaluation?, * 3+ years of professional Python, with production experience building and operating services

 * 1+ years of hands-on work with LLMs in production: prompt/context engineering, tool/function calling, structured outputs, RAG
 * Working knowledge of LangChain/LangGraph or a comparable framework like AgentCore Strands, CrewAI, or Semantic Kernel
 * Experience with LLM observability tools: Amazon CloudWatch, LangSmith, Langfuse, MLflow, or OpenTelemetry
 * Experience designing evaluation frameworks (MLFlow, DeepEval, LLM-as-judge, multi-turn regression)
 * Fluency with Git, Docker, and modern API frameworks
 * Clear written communication and the judgment to know when something is ready to ship

A bachelor's degree is not required. Equivalent practical experience: including bootcamps, self-taught work, career changes, or non-CS technical degrees counts.
Strongly Preferred

 * Hands-on experience with Amazon Bedrock and/or AgentCore as a developer: runtime, gateways, memory, policy, guardrails, observability, awscli, evaluations
 * Experience with Snowflake, Snowpark, or Snowflake Cortex
 * Fluency in writing and reading SQL, as well as understanding semantic models.
 * Familiarity with multi-agent patterns: supervisor/router, subagent/handoff, reflection, human-in-the-loop
 * A considered view on where agents should and shouldn't act and comfort pushing back when "let's add an agent" isn't the right answer
 * Experience in Loyalty, MarTech, AdTech, or a comparable data rich B2B domain

## Description

* Build agent harnesses in Python using LangChain and LangGraph, including tool-calling, structured outputs (Pydantic/JSON schema), retries, streaming, and memory
 * Package agent harnesses for the AgentCore Runtime with appropriate context, tools, skills, and subagents that fit cleanly into production flows and scenarios
 * Write the tools and skills agents use API integrations, SQL queries against Snowflake, Snowflake backed knowledge retrieval with clear contracts and Pydantic validation

Evaluation and Reliability

 * Build evaluation harnesses (golden datasets, LLM-as-judge, regression suites) using AgentCore Evaluations, and wire them into CI
 * Implement guardrails around tool execution: auth scoping, input/output validation, PII and prompt-injection protections, and hallucination mitigation
 * You own what you ship: prototype, deploy through Amazon AgentCore, monitor traces, and fix it when it breaks

Collaboration

 * Partner with data engineers on Snowflake backed retrieval patterns (Cortex Analyst and Cortex Search Services)
 * Contribute to refining our internal engineering patterns as the stack evolves


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