Enterprise Security Software Architect (AI/ML)

Salesforce•San Francisco, CA

About The Position

We are seeking an experienced Security/IAM AI & ML Software Architect to lead the design and evolution of intelligent identity+security platforms powered by data, analytics, and machine learning. This role will define how AI/ML is embedded into the Enterprise Security Technology stack, spanning identity lifecycle, authentication, access governance, PAM, continuous access evaluation, and identity threat detection. You will shape a future-ready, zero-trust-aligned security intelligence architecture that scales across a complex hybrid environment and enables proactive, risk-based, and automated access decisions. You will work closely with Enterprise Security engineering, security data science, platform teams, compliance, infrastructure, and business stakeholders to translate security/identity signals into actionable intelligence and resilient security outcomes.

Requirements

15+ years of software development experience.
7+ years designing and delivering enterprise-scale Security architectures in cloud and hybrid environments.
Demonstrated experience architecting data-intensive, ML-enabled security platforms.
Ability to collaborate with architects, engineering leads, data scientists, and product managers to define long-term architectural vision.
Deep understanding of IAM technologies and protocols: IGA, SSO, RBAC/ABAC, PAM, Directory Services OAuth2, OIDC, SAML, SCIM, LDAP, CAEP
Strong grasp of identity lifecycle, entitlement models, SoD, and compliance frameworks (SOX, NIST, CMMC).
Experience with identity threat detection, continuous access evaluation, and zero trust architectures.
Strong understanding of ML concepts as applied to security, including anomaly detection, classification and risk scoring.
Experience designing data pipelines, feature engineering, model inference paths, and feedback loops.
Familiarity with model governance, explainability, drift detection, and bias considerations in security contexts.
Strong coding skills (Java or Go preferred).
Experience with cloud platforms (AWS preferred)
Experience with CI/CD pipelines.
Experience with Kubernetes, Docker, Terraform.
Experience with observability and monitoring tools

Nice To Haves

Hands-on experience with large-scale identity datasets for anomaly detection and real-time policy enforcement.
Experience architecting risk engines, feature stores, or policy decision services.
Experience supporting security and IAM compliance and data quality uplift campaigns
Architect scalable solutions to collect, analyze, and act upon identity signals, risk scores, and session telemetry.
Certifications: CISSP, CCSP, TOGAF or relevant cloud/security certifications.

Responsibilities

Architecture & Strategy Lead the architectural design for AI/ML enabled IAM capabilities, including: Identity lifecycle and access governance Risk-adaptive authentication and authorization Privileged Access Management (PAM) risk scoring and session analytics Identity threat detection and response (ITDR) Continuous Access Evaluation (CAE) and policy decisioning Define and maintain the Enterprise Identity Intelligence reference architecture, including data flows, ML pipelines, decision engines, and feedback loops. Align IAM AI/ML strategy with business outcomes, security posture, regulatory requirements, and AI principles.
Data, AI & ML Enablement Architect scalable platforms to collect, normalize, enrich, and analyze Security/Identity data, including: User, device and non-human identities Authentication events, access requests, entitlements, sessions, and behavioral telemetry Partner with security data science teams to design, deploy, and operationalize ML models for: Anomaly detection and behavioral baselining, Identity risk scoring and modeling, Privilege misuse and lateral movement detection, Access recommendation, role mining, and policy optimization
Platform & Engineering Leadership Partner with product management and engineering leaders to drive technical roadmaps for the Autonomous IAM platform (API-first, event driven, cloud-native) Define architectural guardrails for identity data usage, model governance, explainability, and auditability. Lead threat modeling and architecture assessments for AI-driven IAM components and their dependencies. Develop proof of concepts to validate ML feasibility, data quality, and performance at scale.
Integration & Enablement Enable IAM administration and onboarding platforms to adopt ML-driven identity standards (for Enterprise Applications) Work with enterprise architects and business leaders to map identity intelligence capabilities to user journeys and business processes. Influence standards for identity APIs, event schemas, feature stores, and policy evaluation frameworks.