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About This Role
Why Stifel:
Stifel strives for a culture that puts its clients and associates first: a culture where everyone belongs, everyone is welcome, and everyone contributes to the success of our clients, their careers, and the firm as a whole.
Let’s talk about how you can find your place here at Stifel, where success meets success. What You'll Be Doing:
The Sr. AI Cloud Platform Engineer is a member of the Security Engineering & Automation team, the security organization's shared build capability, and is responsible for the platform architecture and hands-on engineering of secure, reusable capabilities that enable security teams to operate more efficiently and consistently. This role architects and develops shared security platforms, tool integrations, AI-enabled security capabilities, metrics and dashboarding services, proofs of concept, and automation that removes recurring manual work, built once and designed for adoption by multiple security pods rather than as one-off solutions. Operating with a high degree of autonomy, this role partners with Security Architecture and cloud platform architects to align team-owned solutions with organizational standards and strategic direction. The team operates under a clear contract, Security Engineering & Automation builds, and consuming pods configure, run, and report. What We're Looking For:
- Lead the architecture and technical design of the team's shared capabilities — platforms, services, integrations, APIs, data pipelines, and automation — built to be secure, scalable, resilient, and reusable.
- Build shared security tool integrations and reusable automation that eliminate manual toil, reduce duplicated engineering effort, and improve consistency across security pods.
- Architect AI solutions that both secure the organization's use of AI and apply AI to improve security operations, analysis, and decision support.
- Build and maintain shared metrics, data, and dashboarding capabilities, including common data models and integration patterns, that enable automated, business-readable reporting across the security organization.
- Partner with Security Architecture to embed security requirements, approved patterns, controls, and risk decisions into platform designs, and collaborate with enterprise and cloud platform architects to align with broader technology strategies and standards.
- Translate business, security, operational, and regulatory requirements into platform designs and executable implementation plans.
- Develop infrastructure as code using Terraform, CloudFormation, or CDK, and build automation and integrations using Python, Bash, PowerShell, AWS Lambda, Step Functions, APIs, and event-driven and workflow orchestration services.
- Embed identity and access management, secrets management, encryption, network controls, data protection, logging, monitoring, and policy enforcement into team-owned solutions.
- Conduct proofs of concept and technical evaluations to validate solution viability and inform build-versus-buy decisions, assessing technologies for technical fit, security, interoperability, supportability, cost, and reuse potential.
- Create reusable reference architectures, implementation patterns, technical standards, and onboarding guidance, and contribute to the team's service catalog, including available capabilities, engagement criteria, and ownership expectations.
- Define consumption, configuration, and support boundaries for team-built services in partnership with product management and consuming pods, along with measurable outcomes such as adoption, toil reduction, onboarding time, and data freshness.
- Maintain the reliability, availability, observability, and security of shared team-owned platforms and their CI/CD and operational tooling, and support incidents and root-cause analysis while pod-specific operations, triage, and tool administration remain with the consuming pod.
- Publish architecture diagrams, decision records, runbooks, and service documentation, and communicate evaluations, proof-of-concept results, roadmaps, and delivered business value through planning forums, demonstrations, and stakeholder reviews.
- Mentor engineers and promote strong platform engineering, secure development, automation, and architecture practices within the team.
What You'll Bring:
- Strong analytical and problem-solving skills with the ability to identify manual toil and design reusable capabilities that eliminate it.
- Ability to effectively communicate technical topics and architectural decisions to technical and non-technical audiences.
- Deep understanding of platform architecture principles, including scalability, reliability, observability, interoperability, and cost management.
- Understanding of AI and LLM-based systems, including generative AI, agentic workflows, and their security considerations.
- Demonstrated ability to operate independently, lead technical design efforts, prioritize workload, and collaborate across organizational boundaries.
- Experience architecting and developing reusable cloud or platform capabilities — integrations, automation, APIs, or data pipelines — used by multiple teams.
- Experience conducting proofs of concept, technical evaluations, and build-versus-buy assessments.
- Experience incorporating security requirements and controls into cloud and platform solutions in partnership with Security Architecture or comparable governance functions.
- Experience designing for measurable outcomes such as adoption, toil reduction, reliability, and onboarding efficiency.
Education & Experience:
- Minimum Required: Bachelor's degree in Computer Science, Information Systems, Computer Engineering, Software Engineering, Data Science, Cybersecurity, or related field, or a related combination of education and experience.
- Minimum Required: 6+ years of platform, infrastructure, DevOps, cloud, automation, integration, security engineering, or software engineering experience; 3+ years of hands-on AWS experience in production environments.
- Minimum Required: Experience applying AI, machine learning, or generative AI to automation, engineering, or security use cases.
- Preferred: Experience with metrics platforms, APIs, or workflow orchestration.
- Preferred: Experience supporting regulated or financial-services environments.
Licenses & Credentials:
- Minimum Required: AWS Certified Solutions Architect — Associate or Professional, or equivalent demonstrated AWS expertise.
- Preferred: AWS Certified DevOps Engineer — Professional.
- AWS Certified Security — Specialty, AWS Certified Machine Learning — Specialty, HashiCorp Terraform Associate, CCSP, CISSP, or comparable cloud, AI, or platform engineering credentials, is a plus.
Systems & Technology:
- Deep hands-on AWS experience, including services such as EC2, VPC, IAM, Lambda, ECS/EKS, S3, RDS, CloudWatch, Organizations, and Control Tower.
- Experience with infrastructure as code (Terraform, CloudFormation, or CDK) and scripting and automation using Python, Bash, PowerShell, or similar languages.
- Experience designing and integrating APIs, event-driven services, workflow automation, and third-party security technologies for adoption by multiple teams.
- Experience with AI and machine learning services such as Amazon Bedrock, Amazon SageMaker, Azure OpenAI, or OpenAI APIs, including generative AI, retrieval-augmented generation, agentic workflows, and model gateways.
- Working knowledge of cloud and platform security, including IAM least privilege, secrets management, encryption, network security, logging, monitoring, data protection, and policy-as-code.
- Preferred: Metrics, dashboarding, and data platforms such as Grafana, Power BI, QuickSight, or OpenSearch.
- Preferred: CI/CD tooling (GitHub Actions, Azure DevOps, or Jenkins), container orchestration (Docker, Kubernetes, or ECS/EKS), and observability tooling (Datadog, Prometheus, or OpenTelemetry).
- Preferred: Experience with OPA, AWS Config, Service Control Policies, cloud security posture management, data-loss prevention, or automated guardrail technologies.
About Stifel:
Stifel is more than 130 years old and still thinking like a start-up. We are a global wealth management and investment banking firm serious about innovation and fresh ideas. Built on a simple premise of safeguarding our clients’ money as if it were our own, coined by our namesake, Herman Stifel, our success is intimately tied to our commitment to helping families, companies, and municipalities find their own success.
While our headquarters is in St. Louis, we have offices in New York, San Francisco, Baltimore, London, Frankfurt, Toronto, and more than 400 other locations. Stifel is home to approximately 9,000 individuals who are currently building their careers as financial advisors, research analysts, project managers, marketing specialists, developers, bankers, operations associates, among hundreds more. Let’s talk about how you can find your place here at Stifel, where success meets success.
At Stifel we offer an entrepreneurial environment, comprehensive benefits package to include health, dental and vision care, 401k, wellness initiatives, life insurance, and paid time off.
Stifel is an Equal Opportunity Employer.
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 Stifel, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
Stifel AI Hiring
Stifel has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in St. Louis, MO, US.
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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