Senior AI Agent Engineer

MACHINE LEARNING TECHNOLOGIES LLC; Charlotte (NC); Remote; USD 78,000 - 112,000; Full time

Benefits offered by this job

Remote work possible

Job summary

MACHINE LEARNING TECHNOLOGIES LLC is seeking a Senior AI Agent Engineer in Charlotte, NC with remote flexibility. The role focuses on designing and deploying production-grade AI agents across the full lifecycle, with emphasis on orchestration, governance, and secure deployment.

You will integrate agent systems with MCP, Temporal, and enterprise data stores. Qualified candidates have 7+ years in software, 1–2+ years with AI agents, and strong experience with Python/Java, CI/CD, and knowledge

Qualifications

• 7+ years in software engineering or enterprise-grade solutions.
• 1–2+ years building LLM-based applications/AI agents.
• 3+ years production software with Python/Java.
• 1+ year in AI agent orchestration and MCP/tool calling.
• 1+ year with agent platforms (AWS Agent Core, Kiro).
• 1+ year with Temporal or similar engines.
• 2+ years automating code-change workflows and test generation.
• 1+ year applying context engineering to AI workflows.
• 2+ years with Kafka, MongoDB, SQL, or SQLite.
• 1+ year with Neo4j/Cypher and knowledge graphs.
• 3+ years integrating apps with CI/CD and enterprise tools.
• 2+ years implementing secure software practices and audit trails.
• 1+ year implementing AI governance and guardrails.
• 3+ years SDLC, automated testing, release processes.

Responsibilities

• Design, build, and harden production-grade AI agents across the lifecycle: detect → store → plan → remediate → deploy.
• Develop and orchestrate Finder, Planner, and Executor agents for issue prioritization and controlled deployments.
• Integrate agents with MCP, tool calling, and agent frameworks; use Temporal/workflows.
• Connect agents to Overwatch, UVM vulnerability sources, and enterprise knowledge graphs.
• Implement event processing and data storage with Kafka, MongoDB, SQL/SQLite; query Neo4j/Cypher.
• Enable automated code-change workflows, AI-assisted code generation, and lockdown-test practices.
• Embed governance controls, human approvals, and security in agent design and deployment.
• Collaborate with cross-functional teams; provide technical guidance and documentation.

Skills

LLM-based apps

Python/Java

GitLab CI/CD

AI governance

Agent orchestration

Temporal workflows

Data stores

Knowledge graphs

Education

Bachelor's degree in CS or related field

Tools

Temporal

Kafka

MongoDB

SQL/SQLite

Neo4j/Cypher

GitLab

Job description

Job Overview

• Job ID: J54932
• Job Title: Senior AI Agent Engineer
• Location: Charlotte, NC (Remote)
• Duration: 24 Months + Extension
• Domain Exposure: Consumer Staples
• Client: To Be Discussed Later
• Employment Type: W-2, 1099, C2C
• Work Authorization: US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other
• Bill Rate: $69/hour (C2C)

Job Description

Design, develop, and harden production-grade AI agents across the end-to-end detect → store → plan → remediate → deploy lifecycle.

Build and orchestrate Finder, Planner, and Executor agents to identify issues, prioritize remediation, execute approved actions, validate changes, and support controlled deployments.

Develop agent orchestration and tool integrations using Model Context Protocol (MCP), tool calling, agent frameworks, and workflow engines such as Temporal.

Develop and integrate agents with the Actionable Observation store, Overwatch state machine and prioritization capabilities, and operator console.

Integrate agents with GitLab pipelines, GitLab CI/CD, JFrog Artifactory, canary deployment workflows, and Tenable Vulnerability Management (UVM) as a vulnerability-finding source.

Expose and consume enterprise knowledge graphs containing data lineage and environment context through MCP, ensuring agents have governed, read-only access to relevant information.

Implement event processing, data storage, and state management using Kafka, MongoDB, SQL, and SQLite.

Develop and query knowledge graphs using Neo4j and Cypher to support contextual retrieval, dependency analysis, and remediation planning.

Implement automated code-change workflows, AI-assisted code generation, test generation, and lockdown-test practices to validate remediation changes before deployment.

Apply context engineering and specification-driven development to improve agent accuracy, consistency, traceability, and maintainability.

Embed human-approval gates, audit trails, access controls, and AI governance controls throughout agent design, execution, and deployment.

Ensure autonomous workflows comply with enterprise security standards, change-management processes, deployment controls, and production-readiness requirements.

Support vulnerability remediation and software version-currency initiatives using CVE, EPSS, CVSS, End-of-Life (EOL), and End-of-Support (EOS) considerations.

Implement controlled deployment patterns, including canary releases and incremental rollouts, to reduce operational risk.

Troubleshoot agent execution failures, integration issues, workflow exceptions, and deployment problems; perform root-cause analysis and implement corrective actions.

Monitor and improve agent reliability, performance, scalability, and observability using Dynatrace or comparable tools where applicable.

Collaborate with application development, infrastructure, cybersecurity, DevSecOps, architecture, and production support teams to integrate agents into existing workflows.

Document reusable agent patterns, technical designs, integration approaches, governance controls, onboarding procedures, and operational best practices.

Coach application, infrastructure, and security teams on agent onboarding, secure usage, human oversight, and adoption of the autonomous operating model.

Participate in technical design reviews, code reviews, automated testing, release planning, and continuous improvement initiatives.

Own AI agent solutions across requirements, design, development, integration, testing, deployment, monitoring, and ongoing enhancement.

Basic Qualifications

• Minimum 7+ years of experience in software engineering or production application development, delivering and supporting enterprise-grade solutions.
• Minimum 1–2+ years of hands-on experience developing LLM-based applications, Generative AI solutions, AI agents, or agentic workflows.
• Minimum 3+ years of hands-on experience developing production software using Python and/or Java.
• Minimum 3+ years of experience with Git, GitLab CI/CD, automated testing, and software delivery practices.
• Minimum 1+ year of hands‑on experience with AI agent orchestration, MCP, tool calling, and agent frameworks or equivalent technologies.
• Minimum 1+ year of experience with agent development platforms such as AWS Agent Core, Kiro, or equivalent technologies.
• Minimum 1+ year of experience with workflow orchestration engines such as Temporal or equivalent platforms.
• Minimum 2+ years of experience developing automated code-change workflows, code validation, automated test generation, or lockdown‑test practices.
• Minimum 1+ year of practical experience applying context engineering and specification‑driven development to AI or software engineering workflows.
• Minimum 2+ years of experience working with event‑processing or data‑storage technologies such as Kafka, MongoDB, SQL, or SQLite.
• Minimum 1+ year of experience with Neo4j, Cypher, graph databases, or knowledge‑graph querying.
• Minimum 3+ years of experience integrating applications with CI/CD pipelines, artifact repositories, deployment platforms, or enterprise tools.
• Minimum 2+ years of experience implementing secure software engineering practices, access controls, audit trails, or human‑in‑the‑loop approval mechanisms.
• Minimum 1+ year of experience implementing AI governance, security guardrails, or operational controls for AI‑driven automation.
• Minimum 3+ years of experience with SDLC, automated testing, change management, release processes, and production support.
• Minimum 3+ years of experience troubleshooting complex application or integration issues, conducting root‑cause analysis, and implementing corrective actions.
• Minimum 2+ years of experience collaborating with cross‑functional teams, including application development, infrastructure, DevOps, or cybersecurity.
• Minimum 1+ year of experience documenting technical designs, reusable engineering patterns, operational procedures, or implementation standards.
• Minimum 1+ year of experience providing technical guidance, mentoring engineers, or coaching teams on engineering practices or new technology adoption.
• Demonstrated ability to explain personal technical contributions, agent architecture, implementation decisions, challenges, and business outcomes from previous projects

Equal Opportunity Employer

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.