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About This Role
We are seeking an engineer to accelerate enterprise AI adoption by building, integrating, and optimizing secure access to approved AI tools and model endpoints through web security gateways, proxy infrastructure, and AI security controls. This role sits at the intersection of AI platform engineering, network/security architecture, and developer enablement. The engineer will design and operate the technical controls that allow employees and applications to use sanctioned LLMs safely, reliably, and with acceptable performance. This includes secure routing, policy enforcement, DLP integration, model access governance, observability, and latency optimization across secure web gateways, CASB, API gateways, reverse proxies, and related control planes.
In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Principal Cyber Security Engineering position at the Vice President level, which is part of the job family responsible for providing specialist cyber expertise and creating solutions that protect the organization's systems and networks against actual and potential security threats and vulnerabilities.
Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.
What you'll do in the role:
- Design and implement secure access patterns for enterprise AI services behind secure web gateways, AI gateways, proxies, and cloud security controls.
- Enable access to sanctioned LLMs and AI applications while blocking or restricting unsanctioned services.
- Partner with security, infrastructure, legal, risk, and application teams to onboard AI use cases in a controlled manner.
- Build traffic-routing, authentication, and policy-enforcement mechanisms for user-to-AI and app-to-AI interactions.
- Integrate DLP, data classification, logging, redaction, token filtering, and content inspection into AI traffic flows.
- Optimize latency, throughput, reliability, and user experience for AI access paths traversing security gateways.
- Develop guardrails for prompt/response inspection, data exfiltration prevention, tenant isolation, and approved use enforcement.
- Implement monitoring and alerting for model usage, blocked events, policy violations, response times, error rates, and adoption metrics.
- Support evaluation and rollout of technologies such as secure web gateways, CASB, SSE/SASE, AI gateways, API management, and service mesh controls.
- Create reusable patterns, reference architectures, and onboarding playbooks for AI application teams.
- Perform root-cause analysis for performance bottlenecks, access failures, and policy conflicts affecting AI use cases.
- Contribute to standards for AI access, third-party model connectivity, secrets management, and auditability.
Document architecture decisions, operational procedures, and control evidence for internal governance and regulatory review. *
What you'll bring to the role:
- Bachelor’s degree in Computer Science, Engineering, Cybersecurity, or related field, or equivalent practical experience.
- 5+ years of experience in engineering and design of web security and ZTNA platfoms in Netskope, Zscaler, Palo Alto, Fortigate.
- Fluency in AI application protocols such as WebSocket, Server Sent Events, gRPC, and experience in troublshooting AI connverastions from OpenAI, Anthropic, Grok, MSFT Copilot, Cowork, for both GenerativeAI and AgenticAI
- Hands-on experience with secure web gateways, proxies, CASB, SSE/SASE, WAF, API gateways, or related access-control technologies.
- Experience integrating enterprise applications with REST APIs and cloud services.
- Strong understanding of authentication and authorization patterns, including SSO, OAuth, OIDC, mTLS, and service-to-service identity.
- Experience with DLP, data security controls, logging pipelines, and policy enforcement in enterprise environments.
- Strong scripting or programming skills in Python, Go, Java, or similar.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Ability to troubleshoot across network, security, application, and platform layers.
- Strong written and verbal communication skills with the ability to explain technical tradeoffs to security, risk, and business stakeholders.
Preferred Qualifications
- Experience securing or enabling access to LLMs, GenAI platforms, or model APIs.
- Experience with AI gateways, model routing, prompt filtering, token-aware controls, or AI observability tools.
- Knowledge of enterprise controls around data residency, privacy, records retention, and regulated data handling.
- Experience with tools such as Netskope, Zscaler, Palo Alto, Cloudflare, Akamai, Azure API Management, Kong, Apigee, or similar platforms.
- Familiarity with Kubernetes, service mesh, reverse proxies, and zero-trust access patterns.
- Experience supporting regulated environments such as financial services, healthcare, or public sector.
- Exposure to model risk, AI governance, or third-party technology risk frameworks.
Key Skills
- Secure web gateway architecture
- API security
- AI/LLM platform integration
- DLP and data governance
- Network and application troubleshooting
- Cloud security
- Identity and access management
- Observability and performance engineering
What Success Looks Like
- Approved AI tools and model endpoints are onboarded quickly and safely.
- Latency introduced by gateway and security controls is minimized and measurable.
- Unsanctioned AI usage is reduced through effective routing and enforcement.
- Sensitive data is protected with strong inspection, redaction, and blocking controls.
- Developers and end users can reliably access sanctioned AI services with clear support models.
- Architecture, logs, and controls are well documented and auditable.
WHAT YOU CAN EXPECT FROM MORGAN STANLEY:
At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit : https://www.morganstanley.com/people-opportunities/eeo .
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 Morgan Stanley, 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.
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.
Morgan Stanley AI Hiring
Morgan Stanley has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Dallas, TX, US, Baltimore, MD, US, New York, NY. Compensation range: $195K - $195K.
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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