• Design, implement, and maintain data security controls across cloud platforms, applications, databases, and data pipelines to protect sensitive and regulated information. • Develop and operate data protection capabilities including data classification, encryption, key management, data loss prevention (DLP), and access governance. • Monitor, investigate, and respond to data security incidents, conducting root-cause analysis and driving remediation efforts. • Partner with engineering, platform, and compliance teams to embed security requirements into system design, software development, and data lifecycle processes. • Assess security risks, perform technical reviews, and support compliance with regulatory and industry standards such as GDPR, ISO 27001, NIS2, and customer security requirements.

Role Description We are looking for a senior engineer to design, build and deploy autonomous security agents on the Microsoft stack. You will automate security operations across SOC, vulnerability management and attack-surface use cases — turning security requirements into working agents that gather information, reason over it, act, and hand off cleanly. Key Responsibilities - Design, build and deploy autonomous security agents using Microsoft Security Copilot and Azure AI Foundry Agent Service - Build with the Microsoft Sentinel MCP (Model Context Protocol) server and develop custom MCP tools and servers for security use cases - Engineer detection content in KQL across Microsoft Sentinel and Defender XDR - Build response automation and SOAR playbooks using Logic Apps and Sentinel automation rules - Build autonomous SOC triage agents that classify alerts as true or false positive, enrich entities and assign the right owner - Build agents for vulnerability management and attack-surface reduction including asset discovery, noise reduction and rule-based notifications - Join client calls to gather requirements, discuss technical design and support deployment Essential Skills - Microsoft Sentinel and KQL - Microsoft Defender XDR - Microsoft Security Copilot - Azure AI Foundry Agent Service - Model Context Protocol (MCP) including building custom tools or servers - Logic Apps and security automation (SOAR) - Python and PowerShell - Strong communication skills in client-facing settings Desirable Skills - Microsoft Agent Framework or Semantic Kernel - Microsoft Entra and Purview - Experience with autonomous SOC or AI SOC tooling - Retrieval-augmented generation (RAG) and prompt or guardrail design Certifications - Essential: SC-200 (Security Operations Analyst) or equivalent hands-on experience - Desirable: AI-102 / AI-103, SC-100, AZ-104 How You Will Work This is a project-based engagement delivered primarily offshore. You will report to senior leadership. You are a builder first, but must be comfortable joining client calls to gather requirements, discuss technical design and support deployment. A portfolio of production agents, automations or detection content carries more weight than certifications alone.

• Security Assessment Ownership - Lead security assessments for specific components within client engagements. • Vulnerability Discovery and Analysis - Find and validate vulnerabilities in application code and systems. • Custom Security Tooling - Design and build security testing tools and automation for vulnerability detection. • Architecture and Threat Modeling - Conduct threat modeling and architecture reviews of software systems. • Client Communication - Translate technical findings into clear, actionable recommendations for engineering teams. • Research and Innovation - Contribute to security research initiatives and stay on the cutting edge.

Senior Security Engineer, AI - ML, National Security, Public Sector Location: Washington D.C. United States areGoogleplaceWashington D.C., DC, USA; Maryland, USAlaptop_windowsRemote eligible Job Description: Candidate must work 5 days per week on-site in Fort Meade, Maryland In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: - Health, dental, vision, life, disability insurance - Retirement Benefits: 401(k) with company match - Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment - Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance - Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks - Baby Bonding Leave: 18 weeks - Holidays: 13 paid days per year Note: Google's hybrid workplace includes remote and in-office roles. By applying to this position you will have an opportunity to share your preferred working location from the following: In-office locations: Washington D.C., DC, USA. Remote location(s): Maryland, USA. Minimum qualifications: - Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related technical field or equivalent practical experience. - 5 years of experience in AI/ML development, AI infrastructure engineering, or software development. - 5 years of experience with containerization (Docker) and orchestration (Kubernetes). - 5 years of experience with Python and with libraries like PyTorch, TensorFlow, or Hugging Face Transformers. - Ability to travel up to 25% of the time as needed. - Must possess an active Top Secret/SCI security clearance with current polygraph. Preferred qualifications: - 5 years of experience in AI/ML research or software development. - Experience with LLM deployment frameworks such as vLLM, NVIDIA Triton, or Ollama and agent development. - Knowledge of open worldwide application security project (OWASP) for LLMs or similar security frameworks. - Familiarity with cloud-native AI services (e.g., cloud computing platform, Google Vertex AI). - Track record of deploying AI models on air-gapped or on-premises high-performance computing (HPC) systems. About the job Our Security team works to create and maintain the safest operating environment for Google's users and developers. Security Engineers work with network equipment and actively monitor our systems for attacks and intrusions. In this role, you will also work with software engineers to proactively identify and fix security flaws and vulnerabilities. In this role, you will help us build the most resilient AI infrastructure in the world. This role is designed for a technical expert in Artificial Intelligence and Machine Learning, with a primary interest in how those systems can be defended against adversarial manipulation. You will be responsible for the security configuration of AI deployments, from local on-prem GPU clusters to cloud-native environments. You will understand the nuances of LLMs, neural networks, and containerized ML pipelines, and will apply that knowledge to the frontier of security. You will have an understanding of how Large Language Models (LLMs) work under the hood and to develop the next generation of automated defenses and adversarial testing frameworks. Google Public Sector brings the magic of Google to the mission of government and education with solutions purpose-built for enterprises. We focus on helping United States public sector institutions accelerate their digital transformations, and we continue to make significant investments and grow our team to meet the complex needs of local, state and federal government and educational institutions.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities - Architect and manage LLM deployments across on-premises (NVIDIA/AMD) and cloud (cloud computing platform, Google Cloud platform (GCP) environments. Audit multi-agent orchestration, agent construction, and vector databases to map data flows and enforce privilege boundaries. - Use Docker and Kubernetes to orchestrate scalable inference and training environments, optimizing Graphics Processing Unit (GPU) utilization and resource isolation. - Protect model weights, secure data ingestion, and harden inference endpoints across the Machine Learning operations (MLOps) lifecycle. - Investigate and mitigate AI-specific threats (e.g., prompt injection, jailbreaking, data poisoning). Map testing findings to MITRE ATLAS, OWASP for LLMs, and STRIDE models. - Bridge local high-compute clusters and cloud AI services while maintaining a consistent security posture. Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google's Applicant and Candidate Privacy Policy. Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy, Know your rights: workplace discrimination is illegal, Belonging at Google, and How we hire. If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form. Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting. To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes. Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.