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# TELECOMMUTE Senior AI/MLEngineer

- **Company:** InfoVision, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Extract Transform Load (ETL), Amazon DynamoDB, Python (Programming Language), Software Engineering, Large Language Models, Multi-Agent Systems, Prompt Engineering, Spring-boot, Data Lakes, AWS Fargate, Cloudwatch, Data Pipelines, Serverless Computing
- **Published:** September 10, 2026
- **Apply:** https://www.dice.com/job-detail/20d141bb-a297-43b1-b3dc-2691d83ead4a

## About the Role

5+ years of software engineering experience, including hands-on production experience building LLM agents in a code-first framework. Strands Agents and LangGraph are equally acceptable, as are Google ADK, OpenAI Agents SDK, and comparable frameworks. If you have built and operated real agents in any of them, you will pick up Strands quickly.
Business-conscious judgment: you start from the problem and its value, not the technology, and you have walked away from a clever solution because a simpler one served the customer better.
Strong Python; ability to read and contribute to Java (Spring Boot) services.
Hands-on experience with AWS serverless (Lambda, EventBridge, DynamoDB, Step Functions).
Experience with prompt engineering, tool/function-calling design, and evaluating LLM output quality in production.
An appetite for a heads-down building role: you measure your week in shipped code.
Strongly preferred:
Direct experience with Strands Agents and/or AgentCore Runtime (or migrating agent workloads from Lambda/Fargate to a managed agent runtime); LLM-as-judge, agent-to-agent orchestration, and agent governance.
Direct production experience with MCP (Model Context Protocol) servers.
Comfort owning production observability (OpenTelemetry, CloudWatch) for the systems you build.
Nice to have:
Automotive, fintech, or multi-tenant marketplace platform experience.
Familiarity with Bedrock Guardrails, LLM-as-judge evaluation, model cost/latency optimization, and multi-agent orchestration patterns.
Experience with data pipelines (Glue/Athena) or ETL/data lake tooling.