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Metro Bangalore and established fintech brand increase visibility, though senior AI SecOps niche limits broad competition.
Highly domain-specific AI/ML SecOps and supply-chain security skills limit transferability across non-AI roles.
Explicit 8–10 years plus mandatory DevSecOps, cloud, IaC, and programming requirements create stringent filters.
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Own design and implementation of secure, scalable CI/CD pipelines tailored for AI/ML workloads, including LLMs and agentic AI.
Embed automated security controls such as SAST, container/image scanning, and AI-specific safeguards across the AI lifecycle and supply chain.
Collaborate on AI platform infrastructure security, observability, incident response, and governance including adoption of secure AI frameworks and risk management of AI components.
8–10 years of experience in DevSecOps, ML/AI SecOps, or supply chain security.
Proficient in Python, Go, or similar programming languages.
Strong cloud security expertise in Azure, AWS, or GCP environments, especially Kubernetes and Service Mesh.
Experience with Infrastructure-as-Code (Terraform) and GitOps workflows.
Technical SME with deep experience securing AI/ML platform infrastructure and automation pipelines.
Experience integrating AI-specific security practices such as model integrity validation and prompt-injection mitigation.
Background in cloud security for AI workloads and hands-on familiarity with secrets management and provenance/attestation frameworks.