Interested in this MLOps Engineer role at JPMorganChase?
Apply Now →Skills & Technologies
About This Role
JOB DESCRIPTION
As a Senior Lead Cybersecurity Architect at JPMorganChase within the Cybersecurity and Technology Controls organization, you are an integral part of a team that develops high\-quality cybersecurity solutions for AI applications, AI agents, and platform products. You will drive measurable business impact by applying deep technical expertise and structured problem\-solving methodologies to a diverse array of cybersecurity challenges spanning AI, Machine Learning, and agentic systems. You will partner with product, engineering, and risk stakeholders to identify emerging threats and implement scalable controls that enable responsible innovation. You will help set technical direction through clear guidance, hands\-on design reviews, and measurable risk reduction.
We are looking for an experienced AI Systems Cybersecurity Architect to join our team—not only as an AI/ML security subject matter expert, but as someone who is passionate about advancing safe and secure AI at enterprise scale. You'll work in a collaborative, trusting, thought\-provoking environment that values diversity of thought and creative solutions aligned to our customers' best interests. Best yet, you will join a team of highly motivated AI and security professionals who will help you build a strong foundation for a long\-term career at JPMorganChase.
Job responsibilities
- Develop and enhance security strategies, red teaming programs, and solution designs, while troubleshooting technical issues and creating scalable solutions across AI platforms, AI applications, and agentic workflows.
- Design secure, high\-quality AI and software architectures, reviewing and challenging designs and code to ensure adversarial resilience, secure\-by\-default patterns, and appropriate compensating controls.
- Reduce AI, LLM, and agent security vulnerabilities by applying industry standards and emerging AI safety research, and by evolving policies, testing protocols, and technical controls across the full model development lifecycle (MDLC) and agent runtime.
- Collaborate with stakeholders across product, data science, cyber, legal, and risk to understand AI and agent use cases, drive alignment on AI risk tolerance and mitigation priorities, and recommend modifications during periods of heightened vulnerability, incident response, or regulatory change.
- Conduct discovery, threat modeling, and adversarial testing on generative AI, RAG pipelines, ML systems, and AI agents to identify vulnerabilities such as prompt injection, jailbreaking, data poisoning, tool abuse, insecure memory/context handling, and unauthorized action execution.
- Define and assess agent security/safety controls, including authentication and authorization (authN/authZ) for users, services, and tools; secure session management; least\-privilege tool access; and governance for tool/skill registration, enablement, and lifecycle management.
- Provide guidance on secure design, logging, monitoring, and observability for AI applications and agents, including auditability of prompts, tool calls, policy decisions, and model outputs, with controls to support detection, triage, and forensics.
- Evaluate and influence agent harness/orchestration patterns to ensure safe execution boundaries, reliable policy enforcement, and strong controls around delegation, automation, and human\-in\-the\-loop requirements.
- Assess and secure integration patterns for Model Context Protocol (MCP) and similar tool\-connection mechanisms, including authorization models, trust boundaries, data minimization, and controls to prevent exfiltration or unsafe tool invocation.
- Work with platform and cloud security teams to ensure secure infrastructure configuration and alignment with enterprise security architecture, including controls for AI/ML services and agent runtime dependencies.
- Engage with external researchers, vendors, and standards bodies to track emerging AI and agent threats, and translate best practices into actionable guidance and engineering guardrails.
Required qualifications, capabilities, and skills
- 5 years of applied experience in cybersecurity architecture and/or securing AI/ML systems, including architecture reviews and risk\-based control design.
- Practical cloud\-native experience in AWS, GCP and/or Azure, with hands\-on experience using Public Cloud AI/ML services (e.g., SageMaker, Bedrock) and applying enterprise security patterns in production environments.
- Advanced proficiency in one or more programming languages or applications, with the ability to review code and architecture for security and resilience concerns.
- Advanced knowledge of cybersecurity architecture, applications, and technical processes, with considerable in\-depth knowledge in artificial intelligence and machine learning.
- Experience with AI and machine learning concepts and technologies, including notebooks, Python, TensorFlow, PyTorch, and common ML development workflows.
- Solid understanding and practical experience across the model development lifecycle (MDLC), including data acquisition and preparation, model experimentation, training and testing, serving, and MLOps.
- Solid understanding of the AI system attack surface, threats, and mitigating controls across the MDLC, including AI\-specific risks such as prompt injection, training data compromise, unsafe output handling, and retrieval risks.
- Working knowledge of AI agent security/safety fundamentals, including authN/authZ, secure tool/skill use, least\-privilege execution, secure context and memory handling, and requirements for logging and observability suitable for audit and incident response.
- Knowledge of AI safety, AI alignment, and AI cybersecurity concepts and trends, with the ability to translate evolving threats into practical engineering controls.
Preferred qualifications, capabilities, and skills
- Practical experience designing, developing, or securing AI agents following security best practices, including safe orchestration patterns, secure tool connectivity, and controlled autonomy.
- Experience with API security \+ IAM/enterprise authorization, including authentication, authorization, abuse\-prevention controls for AI\-facing and agent\-facing APIs, and OAuth 2\.0, OpenID Connect, and SAML.
- Knowledge of containers and container orchestration (Docker, Kubernetes, Helm) and the security implications of runtime isolation and workload identity.
- Knowledge of cloud infrastructure as code (IaC) (Terraform), including secure\-by\-default patterns and control enforcement.
- Knowledge of networking concepts and protocols (TCP/IP, routing, DNS, DHCP) and how these affect secure deployment and segmentation of AI systems.
- Familiarity with MCP and/or other agent tool\-connection standards, including security implications of tool discovery, trust boundaries, and authorization delegation.
- Preferred certifications (one or more): AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure Data Scientist Associate, AWS Certified Security – Specialty, Microsoft Certified: Cybersecurity Architect Expert, and/or CISSP.
*This role is designated as a High Risk Role (HRR) and is subject to additional pre\-hire screening and/or role\-based requirements in accordance with applicable firm policies*
\#CTC
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission\-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on\-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase \& Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
Salary Context
This $147K-$225K range is above the median for MLOps Engineer roles in our dataset (median: $168K across 34 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($186K) sits 8% below the category median. Disclosed range: $147K to $225K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
JPMorganChase AI Hiring
JPMorganChase has 141 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Jersey City, NJ, US, New York, NY, US, Seattle, WA, US. Compensation range: $120K - $450K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.