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About This Role
Company Description
VERSANT (Nasdaq: VSNT) is an industry\-changing media and entertainment business and home to trusted brands that shape culture, inform audiences, and build lasting connections. It operates across four core markets: political news and opinion, business news and personal finance, golf, and sports and genre entertainment. These markets are served through a powerful portfolio of iconic and innovative brands, including CNBC, MS NOW, USA Network, Golf Channel, Oxygen, E!, SYFY, and Versant's sports division USA Sports, along with complementary digital assets including Fandango, Rotten Tomatoes, GolfNow and GolfPass.
Job Description
Versant is seeking a Director, AI Solutions Architecture to accelerate the adoption of AI across the enterprise. This role serves as a strategic technical leader, helping teams translate emerging AI capabilities into scalable business solutions that drive measurable impact.
Reporting to the VP of Innovation and Emerging Tech, this role will partner closely with Engineering, Product, Cybersecurity, Resilience, Data, and business stakeholders to establish architectural standards, shared platform capabilities, and reusable services that enable teams to safely and effectively build AI\-powered applications and intelligent agents.
This position combines elements of enterprise architecture, platform engineering, applied AI, and technical strategy. The ideal candidate is equally comfortable engaging with senior leaders on enterprise technology strategy, partnering with engineers on implementation details, and rapidly evaluating new AI technologies and capabilities.
Why This Role Matters
AI is becoming a foundational capability across Versant’s business and technology ecosystem. This role will help define how AI solutions are designed, governed, and scaled across the enterprise while accelerating innovation, improving delivery efficiency, and ensuring AI investments deliver meaningful business value.
This is a hybrid position based in New Jersey and requires regular in\-office collaboration with colleagues and stakeholders.
What You’ll Do
Enterprise AI Architecture
- Define and evolve enterprise architecture standards, reference patterns, and best practices for AI\-powered applications and intelligent agent systems.
- Establish scalable approaches for orchestration, retrieval, workflow automation, tool integration, memory management, planning, and human oversight.
- Partner with Engineering, Cybersecurity, and Resilience teams to establish secure development and deployment standards for AI solutions.
- Ensure architectural decisions balance innovation, scalability, security, governance, and operational excellence.
Shared AI Platform Enablement
- Lead the development and adoption of reusable AI platform capabilities, frameworks, templates, and developer tooling.
- Partner with Cloud Operations and Engineering teams to establish deployment, monitoring, evaluation, governance, and operational support patterns.
- Define common services for model access, prompt management, retrieval, observability, policy enforcement, and cost management.
- Drive adoption of shared AI capabilities across business, technology, and operational teams.
Strategic Solution Leadership
- Partner directly with stakeholders across Versant to identify opportunities where AI can create business value.
- Guide teams through technical discovery, architecture reviews, solution design, and implementation planning.
- Help translate complex or ambiguous business challenges into scalable and sustainable technology solutions.
- Provide hands\-on leadership for strategic enterprise initiatives and high\-impact use cases.
AI Innovation \& Technology Strategy
- Evaluate emerging AI platforms, models, frameworks, and technologies.
- Build strong relationships with strategic technology partners and industry leaders.
- Recommend platform investments, architectural improvements, and new capabilities that support enterprise objectives.
- Help ensure Versant’s AI strategy remains aligned with industry advancements and business priorities.
Platform Roadmap \& Adoption
- Partner with Product and Engineering leaders to shape the roadmap for enterprise AI services.
- Identify opportunities to create reusable capabilities that improve speed, consistency, and quality across teams.
- Establish metrics that measure adoption, reliability, developer productivity, and business impact.
- Drive continuous improvement of enterprise AI capabilities and operating models.
Qualifications
What You Bring
Required Qualifications
- 10\+ years of experience in software engineering, solutions architecture, platform engineering, enterprise architecture, or related technical leadership roles.
- Experience designing, building, and deploying enterprise\-scale cloud applications and distributed systems.
- Experience working with modern AI platforms such as OpenAI, Amazon Bedrock, Google Gemini, Anthropic, Salesforce Agentforce, or comparable technologies.
- Strong understanding of APIs, the Model Context Protocol, integration architectures, CI/CD practices, cloud\-native development, observability, security principles, and identity management.
- Experience partnering with technical and business stakeholders to define and deliver complex solutions.
- Strong communication, presentation, and influencing skills with the ability to engage audiences at all levels of the organization.
Preferred Qualifications
- Experience building, deploying, or scaling AI\-powered applications or intelligent agent systems in production environments.
- Experience with agent orchestration frameworks, retrieval systems, evaluation frameworks, MCP implementations, and tool integration patterns.
- Experience developing engineering standards, platform services, and reusable developer capabilities.
- Experience in Solutions Engineering, Forward Deployed Engineering, Applied AI, Developer Platforms, or related disciplines.
- Experience collaborating with leading AI technology providers and emerging AI ecosystems.
How We Do It
Success in this role requires a demonstrated commitment to Versant’s core behaviors:
- Trust – Build secure, reliable, and responsible solutions while operating with integrity and accountability.
- Teamwork – Foster collaboration across business and technology teams to achieve shared outcomes.
- Transparency – Communicate openly, provide clarity on risks and opportunities, and promote informed decision\-making.
- Agility – Adapt quickly to changing priorities, evolving technologies, and emerging business needs.
- Entrepreneurial Spirit – Embrace innovation, challenge assumptions, and proactively pursue new opportunities for growth and impact.
Why You’ll Love It Here
At Versant, you’ll help shape the future of media, entertainment, and technology while working alongside talented teams that are passionate about innovation, collaboration, and impact.
We offer a comprehensive total rewards package designed to support your health, well\-being, professional growth, and long\-term success.
As a hybrid New Jersey\-based employee, you’ll benefit from the flexibility of remote work while maintaining meaningful in\-person collaboration opportunities that help foster strong relationships, accelerate learning, and drive business outcomes.
Additional Information
As part of our selection process, external candidates may be required to attend an in\-person interview with a VERSANT Media employee at one of our locations prior to a hiring decision. VERSANT Media's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law.
If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation. You can submit your request to [email protected].
VERSANT Media is committed to fair and equitable compensation practices. We include a good faith pay range for each position to comply with applicable state and local pay transparency laws and to promote equity across our organization. Actual compensation will be based on factors such as the candidate's skills, qualifications, experience, and location and may include additional forms of compensation and benefits such as health insurance, retirement plans, paid time off, etc.
*VERSANT Media is not accepting unsolicited assistance from search firms for this employment opportunity. All resumes submitted by search firms to any employee at VERSANT via\-email, the Internet, or in any form and/or method without a valid written Statement of Work in place for this position from VERSANT's Talent Acquisition team will be deemed the sole property of VERSANT. No fee will be paid in the event the candidate is hired by VERSANT as a result of the referral or through other means.*
Salary Context
This $190K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Versant, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $190K to $230K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Versant AI Hiring
Versant has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Englewood Cliffs, NJ, US. Compensation range: $230K - $230K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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