- Company Name
- V-Soft Consulting Group, Inc.
- Job Title
- AI DevOps Engineer
- Job Description
-
Job Title: AI DevOps Engineer
**Role Summary**
Responsible for designing, building, and maintaining end‑to‑end AI/ML delivery pipelines that integrate continuous integration, continuous delivery, and security (DevSecOps) practices. Focuses on deploying LLMs and neural network models in cloud environments while ensuring performance, reliability, and compliance.
**Expectations**
- Deliver scalable, secure AI solutions that meet business objectives.
- Apply rigorous testing, monitoring, and rollback strategies for AI components.
- Communicate effectively with data scientists, security teams, and operations to translate technical requirements into actionable plans.
- Maintain high code quality, documentation, and adherence to best security practices for AI systems.
**Key Responsibilities**
- Design blue/green and rolling deployment strategies for AI/ML models.
- Implement version control for code, data, and model artifacts using GitLab, SonarQube, Jenkins, and Artifactory.
- Automate build, test, scan, and deployment pipelines, integrating vulnerability detection and remediation across CI/CD workflows.
- Develop and maintain automated testing frameworks, including model validation tests and evaluation metrics.
- Build and deploy Agentic AI capabilities using frameworks such as LangChain, LangGraph, or CrewAI.
- Execute context engineering and Retrieval-Augmented Generation (RAG) workflows with vector databases.
- Conduct performance tuning, failover testing, and capacity planning for AI workloads in AWS, Azure, or GCP.
- Monitor, troubleshoot, and optimize AI services in production, ensuring reliability and compliance.
**Required Skills**
- Proficiency in Python (mandatory); Java optional.
- 7+ years in DevSecOps, Site Reliability Engineering, or related SRE roles.
- Hands‑on experience with LLMs (Claude, OpenAI) and neural networks.
- Expertise in AI frameworks: TensorFlow, PyTorch, scikit‑learn.
- Cloud platform experience: AWS, Azure, GCP.
- CI/CD tools: Jenkins, GitLab CI, GitHub Actions, Artifactory, SonarQube.
- Automated security scanning: DAST, SAST, IAST tools for code and model vulnerabilities.
- Agentic AI frameworks: LangChain, LangGraph, CrewAI.
- Context engineering, vector database management, and RAG implementation.
- Strong troubleshooting, problem‑solving, and communication skills.
- Familiarity with Agile development practices.
**Required Education & Certifications**
- Bachelor’s degree in Computer Science, Engineering, or a related technical field.
- Relevant certifications in DevSecOps, cloud architecture, or AI/ML (e.g., AWS Certified DevOps Engineer, Microsoft Certified: Azure DevOps Engineer Expert, TensorFlow Developer Certificate) are preferred but not mandatory.