Cloud & ai security architect

As a Cloud & AI Security Architect at Kyndryl, you will be the guardian of confidentiality, integrity, and availability, ensuring organizations are shielded from an ever‑evolving threat landscape. Your expertise will guide clients through assessments, design, implementation, and continual improvement of security controls across cloud, AI, and infrastructure environments. Key Responsibilities Architect and implement secure cloud foundations (landing zones, identity, network segmentation, logging pipelines, key management, secrets handling). Engineer cloud security controls and automate compliance validation using policy‑as‑code where applicable. Build AI‑powered security capabilities, such as: investigation and triage copilots (SOC assistance) automated alert enrichment and case summarization detection engineering accelerators (rule generation, tuning assistance) SOAR workflow augmentation and auto‑remediation suggestions anomaly detection and behavioral analytics on security telemetry Design secure Gen AI architectures: RAG patterns with secure data access, model gateway controls and prompt/data protections, tenant isolation and secrets management, telemetry, monitoring, and evaluation. Integrate enterprise security platforms (SIEM/SOAR, CNAPP/CSPM, CIEM, IAM/PAM, EDR/XDR) into cohesive architectures and automated workflows. Develop reusable assets: reference implementations, accelerators, pipelines, and templates for repeatable delivery. Collaborate with client teams (SOC, cloud platform, app teams) to operationalize solutions—ensuring adoption, reliability, and measurable risk reduction. Support Dev Sec Ops practices: secure CI/CD, Ia C scanning, container security, and Kubernetes guardrails. Required Skills and Experience 5+ years of experience in cybersecurity and/or cloud security with strong hands‑on engineering exposure. Practical experience with AWS, Azure, and GCP security services and cloud‑native patterns. Understanding of cloud logging/telemetry, encryption and key management, network security, identity and access controls. Experience prototyping or delivering AI‑enabled solutions (analytics, automation, copilots, detection acceleration). Familiarity with RAG, embeddings, secure prompt/data handling, and AI telemetry. Exposure to MLOps and LLMOps practices (deployment, evaluation, drift/quality monitoring, change control) is a plus. Familiarity with SIEM/SOAR engineering, playbook design, API integrations, and workflow automation. Scripting and engineering skills (e.g., Python) and comfort working with APIs and data pipelines. Experience with Ia C (Terraform, Cloud Formation, ARM, Bicep), CI/CD integration, and container/Kubernetes security exposure. Ability to produce implementable designs (diagrams, patterns, reference implementations) and support delivery teams through build and operationalization. Certifications (held or targeted): cloud certifications (architect/security). Security certifications (CCSP, CCSK, CISSP, or equivalent) are a plus. #J-18808-Ljbffr