Job description

AI/ML DevOps Engineer Iselin, NJ, 08830 (Hybrid) Charlotte, NC 28262 Lon Term contract This is a hybrid role located in Iselin,NJ and Charlotte, NC offices. Job Description Overview: 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, Google Cloud Platform) • 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 • 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

Requirements