AI DevOps Engineer

7 days ago

Woodbridge, United States Axelon Services Corporation Full time

Title: AI DevOps Engineer (Hybrid) Location: Iselin, NJ or Charlotte, NC | Pay: 85/hrIf you want to know about the requirements for this role, read on for all the relevant information. Job Description An AI DevOps Engineer bridges the gap between artificial intelligence development and operations, ensuring that AI models and systems are deployed efficiently, monitored effectively, and maintained reliably in production environments. Key Responsibilities AI/ML Engineer Implement version control for code, data, and models using GitLab, SonarQube, Jenkins, Artifactory Automate testing frameworks using AI capabilities, including model validation tests Design blue/green deployment strategies using AI capabilities Automated build, scans and deploy including vulnerability remediation capabilities Required Qualifications Bachelor's degree in Computer Science, Engineering, or related field 7+ years of experience in DevSecOps, Site Reliability Engineering, Hands on knowledge of AI tools, Models, practical use case implementation Proficiency in at least one programming language commonly used in AI (Python, Java) Hands-on experience with cloud platforms (AWS, Azure, GCP) Understanding of ML frameworks (TensorFlow, PyTorch, scikit-learn) Experience with CI/CD tools (Jenkins, GitHub, GitLab CI, Artifactory) Hands on experience with automated security vulnerability detection and remediation using security scanning tools in DAST/SAST/IAST scanning space Hands on experience building and deploying Agentic capabilities using AI Agentic tools, processes across the technology and business landscape Skills LLM ( Claude/ OpenAI) with focus on reasoning/agentic use cases xhuatnn Agentic AI framework LangChain, LangGraph, CrewAI Context Engineering MCP Vector databases RAG Python language proficiency is must. Deep understanding of cloud engineering as related AI, DevOps, Automation Strong troubleshooting and problem-solving abilities Excellent communication skills to work with both data scientists and operations teams Familiarity with agile development methodologies Knowledge of security best practices for AI systems Ability to balance technical requirements with business needs

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