Job description

Description: Job Description – Software / AI Native Engineer Location: St. louis/Arlington/NY/NJ Remote is OK | Arlington & NY/NJ is hybrid Day-to-Day Job Duties Design, develop, and productionize AI agents, LLM integrations, and agentic workflows supporting enterprise applications and developer-productivity use cases. Build modern, cloud-native applications and engineering solutions on AWS, integrating AI capabilities into enterprise-grade software platforms. Use GitHub Copilot and/or Claude Code CLI as part of the daily software-development workflow to accelerate coding, testing, debugging, refactoring, and solution delivery. Design and develop microservices, APIs, cloud-native applications, and distributed software components following enterprise software-engineering practices. Develop production-ready integrations using AWS Bedrock, OpenAI, Anthropic APIs, Azure AI Foundry, or equivalent enterprise AI platforms, where applicable. Build and integrate AI-agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Strands, and Bedrock Agents. Build Model Context Protocol (MCP) servers and MCP-enabled integrations to connect AI agents with enterprise systems, tools, data, and services. Develop enterprise Retrieval-Augmented Generation (RAG) solutions, with focus not only on retrieval but also retrieval quality, grounding, evaluation, and production reliability. Build internal developer platforms and engineering-productivity capabilities that allow software teams to incorporate AI-assisted development into their engineering lifecycle. Implement LLM observability and evaluation frameworks using platforms such as Langfuse, LangSmith, Braintrust, Weights & Biases, or equivalent technologies. Incorporate AI governance and responsible-AI controls, including guardrails, prompt-injection defense, audit logging, access controls, and appropriate operational monitoring. Develop proofs of concept and MVPs rapidly and mature successful prototypes into scalable, supportable production solutions. Work across the full engineering lifecycle including architecture, development, unit testing, automated testing, CI/CD, deployment, monitoring, troubleshooting, and production support. Remain actively hands-on with software development and contribute production code as an individual contributor, rather than operating solely in an architecture or management capacity. Collaborate with product owners, architects, engineering teams, security, cloud/platform teams, and business stakeholders to convert AI opportunities into implementable production solutions. Basic Qualifications Minimum 8+ years of overall software engineering experience, with demonstrated experience building enterprise-grade software solutions. Minimum 5+ years of hands-on experience in modern software engineering, including microservices, REST APIs, cloud-native architecture, DevOps, and CI/CD. Minimum 3+ years of strong hands-on AWS experience, developing or operating enterprise applications and services in AWS environments. Minimum 2+ years of hands-on experience building AI/ML, Generative AI, LLM-integrated, or agentic applications, including at least one solution deployed into a production environment. Demonstrated hands-on experience with GitHub Copilot and/or Claude Code CLI as part of the candidate's regular coding workflow, with the ability to provide specific examples of how AI-assisted development has been used. Demonstrated experience personally building an AI agent, LLM integration, or agentic workflow that reached production, rather than only developing demonstrations or proofs of concept. Strong hands-on software-engineering capability in at least one modern programming language such as Python, Java, JavaScript/TypeScript, or equivalent. Strong understanding of enterprise software-engineering principles including APIs, distributed systems, application security, automated testing, version control, observability, CI/CD, and production support. Demonstrated experience developing and deploying applications using cloud-native engineering practices. Must be a currently active hands-on engineer, with individual-contributor coding experience within approximately the last six months. Strong problem-solving skills and the ability to move from business or engineering requirements through design, coding, deployment, and production support. Critical Screening Requirements Candidates should be screened out if they do not demonstrate: Daily hands-on use of GitHub Copilot and/or Claude Code CLI with specific examples. Personal ownership of an AI agent, LLM integration, or agentic workflow that has reached production. Strong AWS experience sufficient to be productive in an AWS-first enterprise environment. Strong core software-engineering depth covering microservices, APIs, DevOps/CI-CD, and cloud-native architecture. Recent active coding experience as an individual contributor. Travel Travel as required based on project and client needs. Degree Bachelor's degree in Computer Science, Software Engineering, Information Systems, Engineering, or a related technical discipline, or equivalent professional work experience. Nice to Have (But Not a Must) 2+ years of experience with modern GenAI/agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Strands, or AWS Bedrock Agents. Hands-on experience building an MCP server or MCP-enabled integration. This should be considered a highly differentiating capability. Production experience with AWS Bedrock, OpenAI APIs, Anthropic APIs, Azure AI Foundry, or equivalent enterprise AI platforms. Experience building internal developer platforms or engineering-productivity products. Experience implementing RAG systems in production, including retrieval-quality measurement and evaluation rather than only vector-database implementation. Experience with LLM observability and evaluation technologies such as Langfuse, LangSmith, Braintrust, or Weights & Biases. Experience implementing enterprise AI governance / Responsible AI frameworks, including model/agent guardrails, prompt-injection defenses, audit logging, monitoring, and security controls. Profiles That Typically Align Well Senior Software Engineer · Staff Software Engineer · AI Engineer · Applied AI Engineer · Forward Deployed Engineer · Platform Engineer · Full-Stack Engineer – AI Products · Member of Technical Staff Profiles That Typically Do Not Align Data Scientist · ML Research Engineer · MLOps-only Engineer · Advisory-only Solution / Enterprise Architect · Engineering Manager without recent hands-on coding · Prompt Engineer

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Must-haves

Job Description – Software / AI Native Engineer
Location: St. louis/Arlington/NY/NJ
Remote is OK | Arlington & NY/NJ is hybrid
Day-to-Day Job Duties
Design, develop, and productionize AI agents, LLM integrations, and agentic workflows supporting enterprise applications and developer-productivity use cases.
Build modern, cloud-native applications and engineering solutions on AWS, integrating AI capabilities into enterprise-grade software platforms.
Use GitHub Copilot and/or Claude Code CLI as part of the daily software-development workflow to accelerate coding, testing, debugging, refactoring, and solution delivery.
Design and develop microservices, APIs, cloud-native applications, and distributed software components following enterprise software-engineering practices.
Develop production-ready integrations using AWS Bedrock, OpenAI, Anthropic APIs, Azure AI Foundry, or equivalent enterprise AI platforms, where applicable.
Build and integrate AI-agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Strands, and Bedrock Agents.
Build Model Context Protocol (MCP) servers and MCP-enabled integrations to connect AI agents with enterprise systems, tools, data, and services.
Develop enterprise Retrieval-Augmented Generation (RAG) solutions, with focus not only on retrieval but also retrieval quality, grounding, evaluation, and production reliability.
Build internal developer platforms and engineering-productivity capabilities that allow software teams to incorporate AI-assisted development into their engineering lifecycle.
Implement LLM observability and evaluation frameworks using platforms such as Langfuse, LangSmith, Braintrust, Weights & Biases, or equivalent technologies.
Incorporate AI governance and responsible-AI controls, including guardrails, prompt-injection defense, audit logging, access controls, and appropriate operational monitoring.
Develop proofs of concept and MVPs rapidly and mature successful prototypes into scalable, supportable production solutions.
Work across the full engineering lifecycle including architecture, development, unit testing, automated testing, CI/CD, deployment, monitoring, troubleshooting, and production support.
Remain actively hands-on with software development and contribute production code as an individual contributor, rather than operating solely in an architecture or management capacity.
Collaborate with product owners, architects, engineering teams, security, cloud/platform teams, and business stakeholders to convert AI opportunities into implementable production solutions.
Minimum 8+ years of overall software engineering experience, with demonstrated experience building enterprise-grade software solutions.
Minimum 5+ years of hands-on experience in modern software engineering, including microservices, REST APIs, cloud-native architecture, DevOps, and CI/CD.
Minimum 3+ years of strong hands-on AWS experience, developing or operating enterprise applications and services in AWS environments.
Minimum 2+ years of hands-on experience building AI/ML, Generative AI, LLM-integrated, or agentic applications, including at least one solution deployed into a production environment.

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Nice to Have (But Not a Must)

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aillmawsazureci/cdrestmlpythonjavajavascripttypescript

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Effort: medium