- Experience: 5+ years of experience in DevOps/DevSecOps engineering, with at least 1–2 years focused on supporting data science, AI, or machine learning workloads in production.

- AI/ML Familiarity: Conceptual or hands -on exposure to AI/LLM frameworks (e.g., LangChain, LangGraph), API -driven integrations, and vector database structures.

- Cloud Architecture: Deep engineering expertise with cloud platforms (specifically Microsoft Azure or AWS) handling virtual networking, secure boundaries (Firewalls, NSGs), and identity management (Azure AD, IAM, Key Vaults).

- CI/CD & DevOps Tools: Mastery of version control and pipeline orchestration platforms such as Azure DevOps, GitHub Actions, or Jenkins.

- Containerization: Proficient in Docker and orchestration platforms like Kubernetes (AKS/EKS).

- Automation & Scripting: Solid scripting capabilities using Python, PowerShell, Bash, or YAML.

- Security Frameworks:



Strong familiarity with OWASP Top 10 (including OWASP Top 10 for LLMs), vulnerability scanners, and automated compliance tools.

Pipeline Automation (MLSecOps): Design, implement, and maintain secure CI/CD pipelines to automate the deployment of AI models, generative AI applications, and multi -agent workflows.

Security Integration (DevSecOps): Embed automated security controls (SAST/DAST, dependency scanning, container vulnerability management) into pipelines. Implement secure runtime guardrails for AI models and LLM wrappers.

Data & Vector Management: Collaborate with AI engineers to monitor, optimize, and secure relational, document, and vector databases (e.g., Pinecone, PGVector, PostgreSQL) utilized in semantic search and RAG pipelines.