Remote Amazon Data Annotation Jobs in Georgia (NOW HIRING)

About the team and what we'll build together

Kobie runs some of the largest loyalty programs in the world. We're building an internal agent platform on Amazon AgentCore that automates analyst workflows, surfaces insights from program data in Snowflake, and gives our teams and clients an LLM-native way to work with complex loyalty logic.

We're looking for a hands-on AI Engineer to ship on that platform: building agent harnesses, writing the tools those agents call, and owning the reliability and evaluation of what goes to production. This is not a research role. You'll prototype, ship, monitor, and iterate on features used by real teams

Our team tends to be people who reason carefully, ship working code,and pick up new tools without a lot of handholding. There's no single path into this role. We value the impact of what you've built and your track record of building things that hold up.

How you will make an impact

Agent Development

• 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

What you need to be successful

Required

• 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