Security Operations Engineer Jobs in New York
Security Operations Engineer jobs in New York are among the most active in the country, driven by demand from financial services, healthcare systems, and critical infrastructure sectors operating at enterprise scale across New York City, Albany, and Buffalo. Major employers with lasting footprints in the state include JPMorgan Chase, IBM, and Northwell Health, each running substantial security operations functions. The most in-demand specialties are SIEM engineering, threat detection, and cloud security operations, with openings at every level from junior analyst-track roles through senior and principal engineers. Find a role that fits below and apply directly.
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Showing 5 of 32+ 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 32 Security Operations Engineer Jobs in New York
Find roles in New York that match your experience and apply in just a few clicks.
Find JobsSecurity Operations Engineer Jobs by City in New York
Where New York roles are concentrated, by current openings.
Security Operations Engineer Job Market in New York
A snapshot from current New York openings, updated as new roles post.
Who's Hiring
- Brex3
- NYU Langone Health3
- Fanduel2
- Amazon1
- Apollo1
Top Industries Hiring
- Technology & Software9
- Banking & Financial Services4
- Education4
- Investment & Asset Management4
- Fintech2
What New York Employers Look For
The qualifications that appear most often in security operations engineer jobs across New York.
- Bachelor's degree in cybersecurity, computer science, or a closely related field
- Hands-on experience with SIEM platforms such as Splunk, Microsoft Sentinel, or IBM QRadar
- Active CompTIA Security+, CISSP, or equivalent industry certification preferred by New York employers
- Proficiency with threat detection, incident response, and security monitoring in enterprise environments
- Experience securing cloud infrastructure on AWS, Azure, or GCP in regulated industries
- Familiarity with NIST, SOC 2, or financial-sector compliance frameworks common in New York organizations
Security Operations Engineer Jobs in New York: Frequently Asked Questions
How do you become a security operations engineer in New York?
The standard path in New York starts with a bachelor's degree in cybersecurity, computer science, or information systems, followed by entry-level work in IT support, network operations, or a junior SOC analyst role. New York does not require a state-issued license for this role. Employers in the state's financial and healthcare sectors consistently look for candidates who hold certifications such as CompTIA Security+, CEH, or CISSP alongside practical experience with enterprise security tooling.
How much do security operations engineers make in New York?
Security operations engineers in New York earn a median of about $134,660 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $83,110 for the lowest 10% to over $216,220 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire security operations engineers in New York?
Employers hiring security operations engineers in New York right now include Brex, NYU Langone Health, and Fanduel, based on current listings on Migrate Mate as of June 2026. New York's concentration of global financial institutions, large hospital networks, and defense contractors makes it one of the most consistent states for security operations hiring year-round.
Which New York cities have the most security operations engineer jobs?
New York, New York City, and Bronx have the most security operations engineer openings in New York. New York City dominates due to its density of financial institutions, media companies, and enterprise headquarters, while Albany draws openings from state government agencies and defense contractors, and Buffalo has seen growth tied to healthcare systems and regional financial services firms expanding their security operations.
Are there remote security operations engineer jobs in New York?
Yes, and more than many technical roles, since security monitoring and SIEM work is largely desk-based and platform-driven. About 34% of security operations engineer openings tied to New York are remote or hybrid as of June 2026, reflecting strong employer flexibility in this specialty. Threat detection, log analysis, and cloud security engineering are the sub-functions most consistently available in fully remote arrangements.
How can I get hired as a security operations engineer in New York with little or no experience?
The most realistic entry path is a junior SOC analyst role, which large New York employers such as JPMorgan Chase, Northwell Health, and IBM use as a feeder into security operations engineering. Many of these organizations run formal early-career programs and rotational tracks open to recent graduates. Candidates who hold CompTIA Security+ and can demonstrate hands-on lab work or a home-lab portfolio stand out, as do those moving laterally from IT help desk or network operations roles within the same organization.
Where can I find and apply to security operations engineer jobs in New York?
You can find and apply to security operations engineer jobs in New York on Migrate Mate, which lists current New York openings updated regularly. Search the listings, find roles that match your experience and target location, and apply directly to the employers posting them.
See All 32 Security Operations Engineer Jobs in New York
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