Senior SOC Engineer - Agentic

$155K - $205K Bethesda, MD, US Senior AI/ML Engineer

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Skills & Technologies

AwsAzureClaudeCrewaiGcpPrompt EngineeringPython

About This Role

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About SANS

Founded in 1989, SANS Institute (SANS) began as a cooperative research and education organization. Over the next 25 years, it grew to become the most trusted and widely recognized provider of information security training and certification in the world. Today, SANS has served more than 300,000 cybersecurity professionals around the world, with more than 60,000 earning Global Information Assurance Certification (GIAC) security certifications—the leading certification that provides assurance to employers that their people and prospective hires can do the job.

At the heart of SANS is a community of practitioners, from auditors and network administrators to CISOs, who share their lessons learned and collaborate on solutions to the challenges they face. These experts, working across corporations, government agencies, and universities, come together to support and strengthen the global information security community.

Why SANS?

At SANS, our culture is defined by three pillars: Mission, Brand, and People.

  • Our Mission is to hire people who understand the importance fighting against cybersecurity threats.
  • Our Brand reflects a commitment to delivering the highest quality training.
  • Our People are grounded in a culture of fairness, honesty, customer focus, and a pragmatic approach.

What You’ll Achieve at SANS

We are seeking a Senior SOC Engineer - Agentic to join our Security team. This role is a dual-focus security engineering role. On one side, this engineer owns the design and buildout of our agentic SOC — building the detection engineering, automation, and AI agent infrastructure that allows a lean security team to operate at the scope of a much larger one.

On the other, they serve as a hands-on security engineer across the full breadth of the program: vulnerability management, cloud security, identity and access management, application security, architecture reviews, and compliance support.

Initially the role will split roughly evenly between these two tracks. As the Agentic SOC matures and AI agents absorb more of the operational workload, the balance will shift — but the security engineering work never goes away. This is a role for someone who wants to do real security engineering today while building the systems that redefine what security engineering looks like tomorrow.

As a Senior SOC Engineer - Agentic, you will:

  • Agentic SOC Buildout + Design the architecture of the agentic SOC. Define how data flows through the detection and response pipeline, how agents are structured and orchestrated, where human-in-the-loop checkpoints belong, and how the overall system evolves as we expand agent autonomy. + Build and own detection engineering. Write, tune, and maintain detections across our internal SIEM environment. Treat detection content as code — version-controlled, peer-reviewed, tested, measured. Establish the lifecycle of design deploy measure tune improve. + Translate SOC operations into automation. Identify the alert triage, investigation, and response work that is ripe for agent augmentation, and define what good looks like for each workflow before it gets handed to an agent. + Build automations and integrations across our stack. Connect our MSSP, EDR, SIEM, cloud infrastructure, and identity platforms through integrations, scripts, and playbooks — both deterministic automation where that is the right tool, and agent-driven workflows where it is not. + Partner with AI Engineering to build and govern the agent stack. Co-design the MCP servers and tool integrations that let agents work with security systems. Contribute to prompt design, evaluation harnesses, and feedback loops — and own the audit trails and checkpoints that keep agent actions safe and accountable. + Operate and improve. Monitor agent performance, investigate failures, tune behavior, and feed real-world outcomes back into the system. The job does not end when something ships.
  • Security Engineering + Vulnerability management. Own the vulnerability identification, prioritization, and remediation tracking program. Work with engineering and infrastructure teams to drive risk reduction across the environment. + Cloud security. Assess and improve cloud security posture across AWS and Azure — identity and access management (IAM) policy review, configuration hardening, cloud-native detection, and ongoing posture monitoring. + Identity and access management. Partner with IT and engineering on identity architecture, access reviews, privilege management, and authentication standards. + Application security. Conduct code reviews, threat models, and security assessments for internal applications and integrations. Partner with development teams to shift security left. + Security architecture reviews. Evaluate new technologies, integrations, and infrastructure changes for security risk. Provide pragmatic, risk-based guidance. + Compliance and audit support. Support security compliance activities, evidence collection, and audit engagements as needed.
  • Respond to incidents. When something real happens, you are part of the response. The agents help; they do not replace you.
  • Perform other related duties as assigned.

What We’re Looking For

Every SANS employee brings something unique. For this role, we’re looking for candidates with:

  • 7+ years in security operations, detection engineering, or security automation, with enough depth to lead engineering efforts independently — and enough intellectual curiosity to be genuinely excited about rebuilding how a SOC works from the ground up. + - First-hand experience with what a well-run SOC looks like — alert triage workflows, escalation paths, investigation patterns, incident response lifecycle.
  • Detection engineering experience: writing and tuning detections, measuring efficacy, managing false positives. Expert-level command of at least one detection query language (KQL, SPL, Lucene, Sigma, or equivalent).
  • MITRE ATT&CK fluency as a working tool, not a reference.
  • Hands-on experience with EDR and SIEM platforms in production environments.
  • Ability to design end-to-end security operations pipelines — from log ingestion and detection through enrichment, triage, investigation, and response — with a clear understanding of where automation fits at each stage.
  • Systems thinking: comfortable reasoning about data flows, dependencies, failure modes, and the operational implications of architectural decisions before they are built.
  • Experience designing for scale and maintainability — architectures that a small team can operate, extend, and recover when things break.
  • Exposure to threat modeling concepts applied to security infrastructure — you do not need to have owned this, but you should be ready to grow into it.
  • Comfort thinking through architectural tradeoffs and documenting your reasoning — this is a skill we will develop together, not a prerequisite.
  • Strong Python — production-quality code with testing, version control, code review discipline.
  • Comfortable building integrations against REST APIs across heterogeneous security tools.
  • Experience with SOAR platforms, security automation frameworks, or equivalent home-grown tooling.
  • Git-based workflows; as-code mindset for detections, automations, and infrastructure.
  • Working knowledge of AWS; experience with Azure or GCP is a plus.
  • Curiosity about how LLM-based agents differ from traditional automation, and where each fits.
  • Willingness to learn agent frameworks, MCP, and prompt engineering on the job, working alongside our AI Engineering team.
  • Side projects, reading, courses, or hobby experiments with LLMs or agents are a real plus — not because we need expertise, but because we need engagement.
  • Comfortable working with JSON and Markdown — the connective tissue of modern agent tooling, API integrations, MCP server schemas, and documentation.
  • Calibrated skepticism about over-automation — willing to say this should be a script, not an agent, or we should not automate this at all.
  • Operator empathy. A small security team will live with what you build. If you cannot sit with them and understand the work, you will build the wrong things.
  • Comfort with ambiguity. This program is being built, not maintained.
  • Unrestricted authorization to work in the USA; visa sponsorship is not available.

Preferred qualifications include:

  • Experience with MSSP-managed detection and response environments.
  • Detection-as-code experience: CI/CD pipelines for detection content, automated testing, content packaging.
  • Prior experience building SOAR playbooks (Tines, Torq, Cortex XSOAR, Splunk SOAR, or equivalent).
  • Incident response experience beyond Tier 1 — you have owned investigations end to end.
  • Cloud detection and response experience: audit logging, threat detection services, cloud security posture.
  • Identity-focused detection experience (Entra, Okta, Active Directory, or similar).
  • Any hands-on experience with LLM tooling: building with LLM APIs, agent frameworks (LangGraph, CrewAI, etc.), or MCP servers — even hobby-level.
  • Familiarity with agentic development workflows — CLAUDE.md, Claude Code, GitHub Copilot/Codex, or similar AI-assisted engineering tools.
  • Prompt injection and LLM adversarial thinking — particularly relevant since security agents constantly read attacker-controlled data.

Benefits and Perks of Working at SANS

We’re committed to fair and equitable compensation. The expected salary range for this position is $155,000 to $205,000 which is comprised of base salary and bonus, depending on geographic location, skills, and experience. At SANS, pay equity and transparency are priorities.

We offer a comprehensive benefits package that supports your total well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:

  • Financial Benefits: Competitive base salary, bonus opportunities, and a 401(k) plan with company match.
  • Health & Wellness: Robust medical, dental, and vision plans; company-provided short term disability; optional long-term disability, supplemental life and AD&D insurance for employees and dependents; voluntary benefits including accident insurance and identity theft protection; fitness and wellness programs; and a company paid employee assistance program (EAP).
  • Time Off & Flexibility: Generous paid time off, including volunteer time.
  • Learning & Development: Access to professional development and SANS training opportunities.

Flexibility and Balance at SANS

We support our colleagues with the tools and flexibility they need to thrive, both professionally and personally. As a primarily remote work environment, we are committed to maintaining strong connections, collaboration, and a vibrant culture across virtual teams. While most roles operate remotely, some positions may require occasional in-person presence depending on role-specific or business needs.

SANS is an Equal Employment Opportunity Employer

SANS is proud to be an Equal Opportunity Employer. We do not discriminate based on race, color, sex, age, national origin, religion, sexual orientation, gender identity, veteran status, disability, or any other federal, state, or local protected class.

If, because of a medical condition or disability, you require a reasonable accommodation during the application process, or to perform the essential functions of a position, please contact SANS Human Resources.

US Job Seekers: Click here to view the “Know Your Rights” poster.

S

ANS complies with all applicable state and local laws regarding the consideration of applicants with arrest or conviction records. For positions governed by federal and/or state banking regulations, SANS will adhere to relevant requirements when evaluating candidates with criminal histories.

Employment eligibility in the United States is required. SANS does not provide visa sponsorship for this position.

Salary Context

This $155K-$205K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Company SANS Institute
Title Senior SOC Engineer - Agentic
Location Bethesda, MD, US
Category AI/ML Engineer
Experience Senior
Salary $155K - $205K
Remote No

About This Role

AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.

Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.

Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At SANS Institute, this role fits into their broader AI and engineering organization.

Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.

What the Work Looks Like

A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.

Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.

Skills Required

Aws (9% of roles) Azure (7% of roles) Claude (4% of roles) Crewai (1% of roles) Gcp (5% of roles) Prompt Engineering (4% of roles) Python (16% of roles)

Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.

Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.

Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.

Compensation Benchmarks

AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($180K) sits 15% below the category median. Disclosed range: $155K to $205K.

Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.

SANS Institute AI Hiring

SANS Institute has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bethesda, MD, US. Compensation range: $205K - $205K.

Location Context

Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).

Career Path

Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.

From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.

The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.

What to Expect in Interviews

Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.

When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.

AI Hiring Overview

The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.

The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).

Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.

The AI Job Market Today

The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.

The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.

AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.

Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.

The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.

Frequently Asked Questions

Based on 11,866 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $212,000. Actual compensation varies by seniority, location, and company stage.
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
About 6% of the 8,943 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
SANS Institute is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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