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About This Role
Resident Engineer, AI Security The Resident Engineer, AI Security serves as a hands-on technical engineer embedded onsite with one of A10's largest financial services customers in Sunrise, Florida. You will sit directly with the customer's AI platform, model risk, and security engineering teams to operate and scale two critical disciplines: runtime AI firewall enforcement and adversarial model testing.
On the runtime side, you will deploy and operate inline inspection of AI traffic — prompts, responses, retrieval context, tool/MCP calls, and agent-to-agent exchanges — managing policies that determine what gets blocked, redacted, rewritten, or logged. On the testing side, you will build and run adversarial evaluation workflows that probe models, RAG pipelines, and agents before and after they reach production.
This role suits an intermediate-level engineer who bridges backend systems development, cloud infrastructure, and practical AI security.
Responsibilities
AI Firewall and Runtime Enforcement
- Deploy, configure, and maintain inline AI traffic inspection across inference paths (self-hosted models, commercial APIs, RAG, and agents)
- Manage proxy topologies, TLS termination/SSLi, streaming/SSE response handling, and proxy routing across modern network stacks using tools like Envoy, NGINX, or HAProxy
- Implement and tune guardrail policies: prompt injection/jailbreak detection, PII/PCI/MNPI data leakage, grounding controls, and token rate limiting
- Enforce authorization, tool-use policies, and identity attribution across REST and gRPC endpoints to ensure every agentic action maps to a verified principal
Monitor baseline false positive/negative rates, shadow-test policy updates, and manage inspection latency to meet production SLA budgets *
Adversarial Model Testing and Red Teaming
- Execute automated red team campaigns against models, vector databases (e.g., PostgreSQL with pgvector), and RAG pipelines covering injection, data extraction, poisoning, and cost-exhaustion risks
- Maintain versioned test suites and automated evaluation harnesses using Python and Go
- Integrate security testing gates directly into the customer’s MLOps, CI/CD, and dev pipelines
Produce structured finding reports with clear reproduction steps, severity assessments, and concrete remediation steps (policy changes, system prompt updates, or infrastructure controls) *
Customer Collaboration & Product Feedback
- Collaborate daily with the customer's platform architects, model risk teams, and security leads on control selection and threat modeling
- Map control coverage to common industry frameworks (OWASP Top 10 for LLMs, MITRE ATLAS, NIST AI RMF)
Feed operational feedback, detection gaps, and bug reports back to A10 Product and Engineering teams to continuously improve the core product *
Required Qualifications
- Experience: 5–7 years of professional experience in software engineering, security engineering, cloud platform engineering, and technical solution delivery
- Programming Languages: Proficiency in Python and Go for building production-grade integrations, microservices, micro-benchmarks, and test automation harnesses
- Data & Middleware: Strong working knowledge of PostgreSQL (schema design, query optimization, indexing) and API architectures using REST and gRPC
- Cloud & DevOps: Hands-on experience deploying and managing workloads in at least one major cloud provider ( AWS, GCP, or Azure ), alongside containerization (Docker, Kubernetes) and CI/CD pipelines
- Networking & Traffic Management: Solid understanding of core networking concepts, L4/L7 traffic management, reverse proxies (e.g., Envoy, NGINX, HAProxy), TLS decryption/inspection, and networking diagnostics (e.g., tcpdump, wireshark, curl, dig)
- AI & Security Foundations: Practical understanding of LLM application stacks (RAG, vector databases, embeddings, tool-calling) and basic exposure to AI/LLM security risks (prompt injection, jailbreaking, data leakage)
- Delivery: Strong written and verbal communication skills with a track record of implementing technical solutions directly within enterprise environments
- Location: Ability to work onsite in Sunrise, FL on a regular, customer-embedded schedule
- Education: Bachelor's degree in Computer Science, Software Engineering, Information Security, or equivalent practical experience
Preferred Qualifications
- Experience working within financial services or highly regulated environments (understanding of PCI DSS, GLBA, SOX, or model risk frameworks like SR 11-7)
- Prior experience in customer-embedded engineering, professional services, or technical field roles at an enterprise security software company
- Exposure to vector extensions in relational databases (e.g., pgvector in PostgreSQL) or specialized vector stores
- Hands-on experience with LLM guardrail frameworks, AI gateways, or red-teaming toolkits (e.g., Garak, PyRIT)
- Familiarity with A10 product suites, web application firewalls (WAAP), or ADC/SSLi technologies
Industry certifications such as AWS/GCP/Azure Security or Developer Solutions Architect, Certified Kubernetes Administrator (CKA), or security certifications (e.g., Security+, OSCP)" *
A10 Networks is an equal opportunity employer and a VEVRAA federal subcontractor. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. A10 also complies with all applicable state and local laws governing nondiscrimination in employment.
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AI Use Guidelines for Interviews: Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.
Targeted compensation guideline: $110,000 - $140,000. Compensation will vary based on number of factors, including market demand for specific skills, role type, job level, and individual qualifications. Final salary offers are determined by considerations including, but not limited to, subject matter expertise, demonstrated skill level, relevant experience, geographic location, education, certifications, and training.
Salary Context
This $110K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At A10 Networks, 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
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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $110K to $140K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
A10 Networks AI Hiring
A10 Networks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Orlando, FL, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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