Lead AI Engineer

Role Summary The AI Platform Engineer will design, build, and operate a secure, resilient AI platform for the Staples Cyber Security team. This hands-on engineering role leverages Databricks, GCP and other approved cloud platform services, robust integration patterns, and production engineering practices to improve Cyber Security effectiveness, efficiency, and response speed. The platform will support secure interaction with Cyber Security tools and data, multi-agent workflows, detection engineering, investigation and response automation, security analytics, knowledge retrieval, and other approved Cyber Security use cases. The role will enable rapid experimentation and streamlined operations while maintaining security, privacy, governance, and operational resilience. Duties & Responsibilities • Build the Cyber Security AI platform: Design, implement, and operate a reusable Databricks-based platform for developing, evaluating, deploying, and monitoring AI applications, agents, and workflows used by the Cyber Security team. • Productize Databricks AI and cloud capabilities: Use governed data access, model and agent development, retrieval-augmented generation, vector search, model serving, evaluation, observability, and workflow orchestration to create production-ready platform services and reference implementations. Leverage GCP cloud services where they are part of the approved architecture. • Enable Cyber Security use cases: Partner with security engineering, SOC, vulnerability management, identity, cloud security, governance, and architecture teams to deliver tool interaction, alert triage, investigation support, detection engineering, threat hunting, response automation, reporting, and productivity solutions. • Engineer multi-agent architectures: Implement clear agent responsibilities, secure tool access, orchestration, state management, failure handling, human approval points, and end-to-end traceability. • Integrate AI memory architectures: Evaluate and integrate short-term, long-term, episodic, semantic, and workflow memory patterns. Define secure storage, retrieval, scope, refresh, protection, retention, and deletion while preventing cross-user or cross-case data leakage. • Integrate Cyber Security tools and data: Build secure connectors, APIs, tool interfaces, and governed data pipelines for SIEM, SOAR, EDR/XDR, vulnerability management, threat intelligence, identity, cloud, ticketing, case management, and related enterprise security systems. • Design for resiliency and safe failure: Implement high availability, retries, timeouts, idempotency, checkpointing, queueing, back-pressure, rate limiting, circuit breakers, graceful degradation, recovery, and disaster-recovery patterns appropriate for security workflows. • Engineer security into the platform: Apply least privilege, workload identity, secrets management, network isolation, encryption, data classification, audit logging, secure tool calling, prompt-injection defenses, output validation, privacy controls, policy enforcement, and human-in-the-loop approvals for consequential actions. • Establish evaluation and observability: Create repeatable testing for quality, grounding, hallucination, safety, security, latency, availability, and cost. Implement traces, metrics, logs, dashboards, lineage, and operational runbooks for production AI workloads. • Create reusable engineering patterns: Develop platform APIs, SDK patterns, templates, CI/CD pipelines, infrastructure-as-code, reference architectures, low/no-code builder interfaces, and onboarding documentation. • Optimize models for cost and quality: Evaluate model selection and routing based on task complexity, security requirements, quality, latency, availability, and cost. Support fallback strategies and controlled experimentation across approved models. Requirements Years of Experience A minimum of 6 years of experience in a cybersecurity role with progressive responsibility. Basic Qualifications • Proficient in cybersecurity frameworks and standards. • Experienced with cloud security, identity management, threat modeling, and incident response. • Experience: 5+ years in security architecture; 3–5+ years designing controls for cloud platforms; hands‑on with AI/ML security or production LLM workloads. • Depth in identity & access: Conditional Access, workload identities, service principals, managed identities, and agent identity governance patterns. • Cross‑cloud fluency especially with AI PaaS services: Azure, GCP, Databricks Mosaic, and Microsoft 365 Copilot/Copilot Studio. • Threat‑driven mindset: Familiar with LLM‑specific risks (prompt injection, data exfiltration, model‑assisted fraud, tool‑calling abuse), secure SDLC for agents, working knowledge of all security control types, and red/blue team techniques adapted to AI. • Controls & observability: DLP, logging/telemetry, SIEM/SOAR integrations, Zscaler/Defender stack, and incident response playbooks tailored for agentic workflows. • Leadership & communication: Proven ability to lead architecture programs, influence cross‑functional stakeholders, write clear standards, and mentor engineers. How you’ll measure success • Guardrail coverage: Measurable adoption of baseline controls and policies across all AI platforms and environments. • Reduced risk: Fewer high‑severity findings in AI threat modeling, pen tests, and control assessments • Operational readiness: SOC detections and runbooks for AI threats in place; effective use of SIEM AI tools; audit‑ready evidence trails. • Governance maturity: AI Governance tools functioning at scale with clear owner/lifecycle controls. • Developer enablement: Adoption of secure patterns and reference architectures by product teams; streamlined approvals that balance agility and control. Preferred Qualifications • Recognized cybersecurity certification (e.g., CISSP, CISM, CCSP, GIAC). • Cloud security certifications (Azure/GCP) • Familiarity with state‑level AI legislation, FTC guidance, and other international AI legislation