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About This Role
Posted Date
3/23/2026
Description
WHO WE ARE AND WHAT WE DO
The Hallmark Technology team is comprised of innovative technologists who are on the forefront of business transformation. Hallmark’s growth strategy is reliant on a technology transformation across all lines of business. We are on a journey to reimagine, scale and simplify how our core infrastructure supports the evolution of new capabilities, consumer experiences and the beloved Hallmark\-branded businesses. Achieving this aspiration will require a massive increase in technology and organizational transformation activities.
Technology capabilities Hallmark will require and implement:
- Digital and Direct to consumer (DTC) technologies across our media and retail business
- Advance data management capability, connected to new analytics and AI platforms
- Advancing the cloud\-based application architecture and evolution our ERP/Core platforms
- An integration strategy to ensure seamless connection between on premise and cloud\- based applications
A people and digital workplace strategy the continues to put the employee at the heart of technology and collaboration tools
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WHAT YOU’LL DO \& HOW IT SHAPES OUR SUCCESS
The Director of Architecture for Artificial Intelligence (AI) serves as a strategic and technical leader within Hallmark’s Enterprise Architecture team. This role is responsible for shaping, guiding, and governing the design and implementation of AI systems and agentic agents that drive innovation, operational efficiency, and emotional connection across the enterprise. The Director of Architecture for AI will lead cross\-functional efforts to realize Hallmark’s AI strategy, ensuring solutions are scalable, secure, ethical, and aligned with Hallmark’s core values. This position plays a key role in defining and executing Hallmark’s broader AI strategy.
Responsibilities:
- AI Architecture Leadership: Define and evolve the AI reference architecture and collaborate with executive leadership to identify AI opportunities that drive value and competitive advantage.
- AI and Innovation Strategy: Lead the technical direction of the Hallmark AI Accelerator Program, ensuring alignment with business goals and the AI Council (AIC) charter.
- Cross\-Functional Collaboration: Partner with data architects, engineers, product managers, legal, privacy, and security teams to deliver responsible AI solutions.
- Governance \& Risk Management: Implement AI governance frameworks, define risk levels, decision rights, and ensure compliance with the Security Architecture Review Board, legal, and regulatory standards.
- Platform Engineering: Stand up and evolve AI platforms, including sandbox environments, vendor selection, and build\-vs\-buy decisions.
- Design and govern architecture for AI agents, multi\-agent systems, and autonomous workflows across enterprise platforms including establishing standards for agent orchestration, memory management, tool integration, and human\-in\-the\-loop oversight.
- Mentorship \& Evangelism: Mentor architects and engineers, and promote AI literacy across the enterprise through training, awareness campaigns, and community engagement.
- AI/ML Systems Design: Proven experience designing and deploying AI/ML systems at scale, including model training, inference pipelines, and integration with enterprise platforms.
- Cloud \& Infrastructure: Hands\-on experience with cloud\-native AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI), GPU/TPU infrastructure, and container orchestration (Kubernetes, Docker).
- Security \& Compliance: Familiarity with AI\-specific security risks (e.g., prompt injection, model extraction), and experience implementing controls aligned with frameworks like NIST AI RMF.
- AI and Data Platform Integration: Partner with enterprise data architecture/governance teams to align AI systems with enterprise data platforms, data modeling, governance, and unified consumer data strategies.
BASIC QUALIFICATIONS
*The following are required to be considered for this role:*
- At least twelve years of combined experience in Information Technology and/or Information Security
- Bachelor’s degree or 4 years’ professional work experience
- At least 10 years’ experience in enterprise architecture or AI/ML systems design
- At least 3 years’ experience in leadership role
- At least 3 years’ experience in Enterprise Architecture modeling frameworks (TOGAF, Zachman, etc.)
- Cloud AI/ML certification (e.g., Azure AI Engineer, AWS AI Practitioner, or equivalent)
PREFERRED QUALIFICATIONS
*Your resume will stand out if you have the following:*
- Master’s degree in computer science, data science, engineering or related field
- Strong understanding of data privacy, security, and regulatory compliance in AI contexts
- Excellent communication and stakeholder management skills
- Strong Experience in Standards (TOGAF, GDPR, ISO/IEC 27001, 27002, 20000\-1, 42001 AI Management) TOGAF or Zachman Framework certification
- Experience with AI\-specific security risks and controls (e.g., prompt injection, model extraction)
- Demonstrated ability to embed fairness, transparency, and explainability into AI systems
- Experience contributing to or leading AI governance boards or councils
- Experience launching or scaling AI innovation and center of excellence programs
- Deep expertise in AI/ML frameworks and Large Language Models (e.g., TensorFlow, PyTorch, Hugging Face, OpenAI, CoPilot, SageMaker, Llama, Grok, Anthropic, etc.) and cloud platforms (Azure, AWS)
- Experience with AI accelerator Platforms and suites (i.e. Azure AI Foundry, AWS Bedrock, H2o.ai, etc.) and API/A2A/MCP connections for LLM integrations to accelerate AI development.
- Knowledge of NIST Cyber Security Framework
- Knowledge of Cloud Governance
- Ability to develop automation scripts
- Strategic Planning: Develop and maintain multi\-year architecture roadmaps aligned with business goals
- Architecture Design: Create models for business, data, application, and technology layers
- Strong grasp of data science, statistics, and algorithm design
- Technology Experience preferred: AWS, Azure, GCP cloud technologies SAP LeanIX or other EA tooling API/MCP/A2A management tools
- Data Tools (Snowflake, Informatica, Power BI, Teradata)
- AI Programming Languages (Python, Java, R, Golang)
Office 365 \& Power Platform (inc. SharePoint, Visio, Power Automate)
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ADDITIONAL DETAILS
- This position offers a hybrid work arrangement, giving you the best of both worlds: collaborating in person at our headquarters in Kansas City, MO and enjoying the flexibility of working remotely.
- This role is eligible for Hallmark’s Annual Incentive Plan. More information will be shared during the interview process.
- Hallmark is committed to recognizing and rewarding performance. Employees are eligible for annual merit\-based increases, aligned with individual and company performance.
- In alignment with our culture of care, Hallmark offers a competitive benefits package, including medical, dental and vision plans, paid time off, 401K with company match, and profit\-sharing.
COME JOIN US! Now’s your chance to embrace a future with Hallmark\- just follow the instructions below to apply.
You must show how you meet the basic qualifications in a resume or document you upload, or by completing the work experience and education application fields. Accepted file types are DOCX and PDF.
In compliance with the Immigration Reform and Control Act of 1986, Hallmark Cards, Inc. and its subsidiary companies will hire only individuals lawfully authorized to work in the United States. Hallmark does not generally provide sponsorship for employment.
Employment by Hallmark is contingent upon the signing of the Employment Agreement, signing of an agreement to arbitrate in connection with the Hallmark Dispute Resolution Program, completing Form I\-9 Employment Eligibility Verification, passing the urinalysis drug screen, education verification and satisfactory reference and background checks.
Hallmark is an equal opportunity employer. All qualified applicants will be considered for employment without regard to race, color, religion, sex, age, pregnancy, national origin, physical or mental disability, genetics, sexual orientation, gender identity, veteran status, or any other legally\-protected status. Principals only please.
HALLMARK – Because Connecting With Each Other Has Never Been More Important
Type
Full\-time
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Information Technology Senior Management Forum, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 44 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Engineering Manager, AI Software Engineer. Positions span McLean, VA, US, Irving, TX, US, Fort Worth, TX, US. Compensation range: $100K - $392K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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