Security Operations Engineer Jobs
Security Operations Engineer jobs are open across defense, financial services, healthcare, and technology, from entry-level analyst roles to senior and staff-level positions, with specializations in threat detection, incident response, and cloud security engineering. Find a role that fits from the openings below and apply directly.
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Showing 5 of 428+ Security Operations Engineer jobs
Job Description
Requisition ID
94174
Department
Tech Data AI Ventures
Job Function
Tech Data AI Ventures
Location
New York, New York, United States
Role Location Designation
Hybrid - 3 days per week
Location Designation: Hybrid - 3 days per week
The AI / ML Security Operations Engineer is a hands-on senior engineering role embedded within the Application Security organization, responsible for securing New York Life's machine learning and AI pipelines as they evolve from isolated experimentation into production, agentic, and automated decisioning systems. This role sits at the intersection of ML engineering, platform engineering, and security, and is accountable for establishing the controls, guardrails, and reference patterns that scale as AI adoption accelerates across the enterprise.
The engineer will be responsible for securing the full ML lifecycle, from data ingestion and feature pipelines through model training, registry, deployment, and execution, with a primary focus on Google Cloud Vertex AI as the enterprise ML platform. Day-to-day work includes building guardrails for agentic and tool-invoking AI use cases, protecting ML supply chain integrity, integrating ML security controls into existing AppSec CI/CD and SSDLC processes, contributing security requirements to ML platform and identity decisions owned by partner teams, and partnering directly with data scientists, ML engineers, and platform owners to operationalize secure-by-default patterns.
This is a senior individual contributor role with strong cross-functional influence expectations. The right candidate has done this work hands-on in a regulated environment and can also define enterprise standards, mentor peers, and engage credibly with risk, audit, and model risk management stakeholders.
What You'll Do:
The engineer will be responsible for securing the full ML lifecycle, from data ingestion and feature pipelines through model training, registry, deployment, and execution, with a primary focus on Google Cloud Vertex AI as the enterprise ML platform. Day-to-day work includes building guardrails for agentic and tool-invoking AI use cases, protecting ML supply chain integrity, integrating ML security controls into existing AppSec CI/CD and SSDLC processes, contributing security requirements to ML platform and identity decisions owned by partner teams, and partnering directly with data scientists, ML engineers, and platform owners to operationalize secure-by-default patterns.
This is a senior individual contributor role with strong cross-functional influence expectations. The right candidate has done this work hands-on in a regulated environment and can also define enterprise standards, mentor peers, and engage credibly with risk, audit, and model risk management stakeholders.
What You'll Bring:
-
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience, with 5+ years in application security, cloud security, or security engineering
-
Hands-on production experience securing at least one major ML platform. Vertex AI strongly preferred, with SageMaker or Azure ML acceptable as transferable experience that will be cross-validated against GCP
-
Strong working knowledge of the end-to-end ML lifecycle and MLOps workflows: data ingestion, feature pipelines, training jobs, model registry, deployment patterns, and online/offline serving
-
Practical understanding of how ML environments should be separated across dev, training, staging, and production, and the ability to partner with platform teams to ensure those boundaries hold from a security standpoint
-
Working knowledge of non-human identities, service accounts, workload identity federation, and automated CI/CD or pipeline-driven workflows, with the ability to evaluate whether identity patterns proposed by partner teams meet security requirements
-
Fluency with AI/ML-specific threat scenarios including data poisoning, model theft, training data exfiltration, inference abuse, prompt injection, indirect prompt injection, unsafe tool invocation, and agentic misuse, and the ability to translate them into concrete controls
-
Hands-on experience integrating security controls into CI/CD pipelines and infrastructure-as-code environments (Terraform, GitHub Actions, GitLab CI, Cloud Build, or equivalent)
-
Working understanding of cloud IAM principles and least-privilege design, sufficient to review and provide security input on identity patterns owned by platform and cloud teams
-
Application security fundamentals: authentication/authorization patterns, supply chain security (SLSA, SBOMs, signed artifacts), secure API design, and secrets management
-
Proficiency in Python for automation, security tooling, and detection logic. Candidates should be able to walk through code they have personally written, not just reviewed
-
Ability to operate as both a hands-on engineer and a pattern-setter, comfortable building the first instance of a control and then turning it into a reusable enterprise standard
Preferred Qualifications
-
Direct experience securing agentic AI systems, orchestration frameworks (LangChain, LangGraph, Vertex AI Agent Builder, ADK, CrewAI), or autonomous tool-invoking workflows in production
-
Working familiarity with AI security frameworks such as MITRE ATLAS, OWASP LLM Top 10, OWASP ML Top 10, NIST AI RMF, Google Secure AI Framework (SAIF), or Databricks AI Security Framework
-
Experience designing governance models for ML platforms in financial services, healthcare, or another regulated industry, including how controls map to model risk management (SR 11-7) and applicable audit requirements
-
Background working alongside data scientists and ML engineers on production model deployments, not just reviewing their work from a security distance
-
Exposure to model risk management, model validation, or model controls partnerships with second-line risk functions
-
Experience with policy-as-code and guardrail enforcement at scale (OPA / Rego, Cloud Custodian, Conftest, Sentinel, or equivalent)
-
Familiarity with detection engineering for ML workloads, including log sources from Vertex AI, model serving endpoints, agent execution traces, and how to write meaningful detections against them
-
Hands-on exposure to LLM gateways, content safety and guardrail products (Lakera, Protect AI, NeMo Guardrails, Llama Guard, Vertex AI Safety Filters), or self-built equivalents
Pay Transparency
Salary Range: $147,500-$211,000
Overtime eligible: Exempt
Discretionary bonus eligible: Yes
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Company Overview
At New York Life, our 180-year legacy of purpose and integrity fuels our future. As we evolve into a more technology-, data-, and AI-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities—inviting bold thinking, collaborative problem-solving, and purpose-driven innovation. Here, you’ll find the rare balance of long-standing stability and forward momentum, supported by an inclusive team that honors tradition while embracing progress.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and deliver solutions that matter. Your ideas drive what’s next, and your growth powers it.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities.
Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners.
Job Requisition ID: 94174
Job Description
Requisition ID
94174
Department
Tech Data AI Ventures
Job Function
Tech Data AI Ventures
Location
New York, New York, United States
Role Location Designation
Hybrid - 3 days per week
Location Designation: Hybrid - 3 days per week
The AI / ML Security Operations Engineer is a hands-on senior engineering role embedded within the Application Security organization, responsible for securing New York Life's machine learning and AI pipelines as they evolve from isolated experimentation into production, agentic, and automated decisioning systems. This role sits at the intersection of ML engineering, platform engineering, and security, and is accountable for establishing the controls, guardrails, and reference patterns that scale as AI adoption accelerates across the enterprise.
The engineer will be responsible for securing the full ML lifecycle, from data ingestion and feature pipelines through model training, registry, deployment, and execution, with a primary focus on Google Cloud Vertex AI as the enterprise ML platform. Day-to-day work includes building guardrails for agentic and tool-invoking AI use cases, protecting ML supply chain integrity, integrating ML security controls into existing AppSec CI/CD and SSDLC processes, contributing security requirements to ML platform and identity decisions owned by partner teams, and partnering directly with data scientists, ML engineers, and platform owners to operationalize secure-by-default patterns.
This is a senior individual contributor role with strong cross-functional influence expectations. The right candidate has done this work hands-on in a regulated environment and can also define enterprise standards, mentor peers, and engage credibly with risk, audit, and model risk management stakeholders.
What You'll Do:
The engineer will be responsible for securing the full ML lifecycle, from data ingestion and feature pipelines through model training, registry, deployment, and execution, with a primary focus on Google Cloud Vertex AI as the enterprise ML platform. Day-to-day work includes building guardrails for agentic and tool-invoking AI use cases, protecting ML supply chain integrity, integrating ML security controls into existing AppSec CI/CD and SSDLC processes, contributing security requirements to ML platform and identity decisions owned by partner teams, and partnering directly with data scientists, ML engineers, and platform owners to operationalize secure-by-default patterns.
This is a senior individual contributor role with strong cross-functional influence expectations. The right candidate has done this work hands-on in a regulated environment and can also define enterprise standards, mentor peers, and engage credibly with risk, audit, and model risk management stakeholders.
What You'll Bring:
-
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience, with 5+ years in application security, cloud security, or security engineering
-
Hands-on production experience securing at least one major ML platform. Vertex AI strongly preferred, with SageMaker or Azure ML acceptable as transferable experience that will be cross-validated against GCP
-
Strong working knowledge of the end-to-end ML lifecycle and MLOps workflows: data ingestion, feature pipelines, training jobs, model registry, deployment patterns, and online/offline serving
-
Practical understanding of how ML environments should be separated across dev, training, staging, and production, and the ability to partner with platform teams to ensure those boundaries hold from a security standpoint
-
Working knowledge of non-human identities, service accounts, workload identity federation, and automated CI/CD or pipeline-driven workflows, with the ability to evaluate whether identity patterns proposed by partner teams meet security requirements
-
Fluency with AI/ML-specific threat scenarios including data poisoning, model theft, training data exfiltration, inference abuse, prompt injection, indirect prompt injection, unsafe tool invocation, and agentic misuse, and the ability to translate them into concrete controls
-
Hands-on experience integrating security controls into CI/CD pipelines and infrastructure-as-code environments (Terraform, GitHub Actions, GitLab CI, Cloud Build, or equivalent)
-
Working understanding of cloud IAM principles and least-privilege design, sufficient to review and provide security input on identity patterns owned by platform and cloud teams
-
Application security fundamentals: authentication/authorization patterns, supply chain security (SLSA, SBOMs, signed artifacts), secure API design, and secrets management
-
Proficiency in Python for automation, security tooling, and detection logic. Candidates should be able to walk through code they have personally written, not just reviewed
-
Ability to operate as both a hands-on engineer and a pattern-setter, comfortable building the first instance of a control and then turning it into a reusable enterprise standard
Preferred Qualifications
-
Direct experience securing agentic AI systems, orchestration frameworks (LangChain, LangGraph, Vertex AI Agent Builder, ADK, CrewAI), or autonomous tool-invoking workflows in production
-
Working familiarity with AI security frameworks such as MITRE ATLAS, OWASP LLM Top 10, OWASP ML Top 10, NIST AI RMF, Google Secure AI Framework (SAIF), or Databricks AI Security Framework
-
Experience designing governance models for ML platforms in financial services, healthcare, or another regulated industry, including how controls map to model risk management (SR 11-7) and applicable audit requirements
-
Background working alongside data scientists and ML engineers on production model deployments, not just reviewing their work from a security distance
-
Exposure to model risk management, model validation, or model controls partnerships with second-line risk functions
-
Experience with policy-as-code and guardrail enforcement at scale (OPA / Rego, Cloud Custodian, Conftest, Sentinel, or equivalent)
-
Familiarity with detection engineering for ML workloads, including log sources from Vertex AI, model serving endpoints, agent execution traces, and how to write meaningful detections against them
-
Hands-on exposure to LLM gateways, content safety and guardrail products (Lakera, Protect AI, NeMo Guardrails, Llama Guard, Vertex AI Safety Filters), or self-built equivalents
Pay Transparency
Salary Range: $147,500-$211,000
Overtime eligible: Exempt
Discretionary bonus eligible: Yes
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Company Overview
At New York Life, our 180-year legacy of purpose and integrity fuels our future. As we evolve into a more technology-, data-, and AI-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities—inviting bold thinking, collaborative problem-solving, and purpose-driven innovation. Here, you’ll find the rare balance of long-standing stability and forward momentum, supported by an inclusive team that honors tradition while embracing progress.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and deliver solutions that matter. Your ideas drive what’s next, and your growth powers it.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities.
Recognized as one of Fortune’s World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners.
Job Requisition ID: 94174
See All 428+ Security Operations Engineer Jobs
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Find JobsSecurity Operations Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- CVS Health51
- Summit Fire & Security18
- Amazon14
- Brex9
- Apple6
Top Industries Hiring
- Technology & Software112
- Healthcare & Medical Services62
- Banking & Financial Services36
- Consulting & Professional Services23
- Investment & Asset Management18
What Employers Look For
The qualifications that appear most often in security operations engineer jobs.
- Three or more years of hands-on experience in a security operations center or equivalent environment
- Proficiency with at least one major SIEM platform such as Splunk, Microsoft Sentinel, or IBM QRadar
- Experience with endpoint detection and response tools including CrowdStrike Falcon or Carbon Black
- Relevant certification such as CompTIA Security+, CEH, GCIA, GCIH, or CISSP
- Familiarity with cloud security monitoring across AWS, Azure, or Google Cloud Platform
- Knowledge of incident response frameworks and threat intelligence integration practices
Tips for Your Security Operations Engineer Job Search
Tailor your resume to the threat stack
List the specific SIEM platforms, EDR tools, and cloud security services you've worked with, not just broad categories like 'security tools.' Recruiters and hiring managers scan for exact tool names like Splunk, CrowdStrike, or Microsoft Sentinel to filter candidates quickly.
Lead with metrics from incident response
Quantify your impact with response time improvements, alert volume reductions, or the scope of environments you've protected. Generic statements about 'monitoring infrastructure' won't differentiate you from dozens of other applicants at the same seniority level.
Apply early to roles that fit
Migrate Mate lists security operations engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Filter openings by clearance requirements early
Many security operations engineer roles in defense and government contracting require an active or adjudicated clearance. Identify clearance requirements in the posting before you spend time on a tailored application to a role you can't currently qualify for.
Prepare a hands-on technical scenario walkthrough
Interviewers frequently ask you to walk through a real incident you investigated, including your detection method, containment steps, and post-incident actions. Prepare two or three concise walkthroughs that demonstrate your decision-making under pressure, not just your tool familiarity.
Negotiate on scope, not just compensation
After an offer, ask about on-call rotation frequency, the escalation structure, and whether the team operates 24/7 or business hours. These factors affect workload more than title, and understanding them before you accept helps you evaluate the role accurately.
Security Operations Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most security operations engineers?
The companies hiring the most security operations engineers right now include CVS Health, Summit Fire & Security, and Amazon, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of June 2026. Demand is concentrated in defense contracting, financial services, and large enterprise technology environments.
How many security operations engineer jobs are remote?
About 27% of security operations engineer openings are fully remote or hybrid as of June 2026, though roles that involve classified systems or government contracting are almost always on-site. Cloud security and threat intelligence-focused positions are the most likely to offer remote flexibility within the broader security operations function.
How do you become a security operations engineer?
Most security operations engineers start in IT support, network administration, or a junior SOC analyst role to build foundational skills in log analysis and network traffic monitoring. From there, earning certifications like CompTIA Security+ or GCIA, gaining experience with a SIEM platform, and handling real incident response cases builds the profile employers expect at the engineer level.
Can you get a security operations engineer job with little experience?
Yes, entry-level security operations engineer roles do exist, but they typically require you to demonstrate hands-on ability rather than years of employment history. Building a home lab, completing platforms like TryHackMe or Hack The Box, earning a recognized entry-level certification, and contributing to capture-the-flag competitions can substitute for professional experience when your resume is otherwise thin.
What does the security operations engineer interview process look like?
The process typically includes an initial recruiter screen, a technical phone interview focused on your tool experience and incident response knowledge, and then a practical stage where you walk through a past investigation or work through a simulated alert triage scenario. Final rounds often involve meeting the broader security team and discussing your approach to threat detection and escalation decisions.
Where can I find and apply to security operations engineer jobs?
You can find and apply to security operations engineer jobs on Migrate Mate, which lists current openings from employers across the United States. Search the listings to find roles that match your experience, specialization, and location preference, then apply directly to each one that fits.
See All 428+ Security Operations Engineer Jobs
Jump back to the full list of openings and apply to any security operations engineer role that fits.
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