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About This Role
Director, Product \& Technology Services (Strategy \& AI Transformation)
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Transform Business Strategy into Measurable Outcomes
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Role Summary
The Director of Product and Technology Services bridges product strategy and IT execution. This role drives enterprise\-wide strategic planning, identifies new market opportunities, and operationalizes modern AI capabilities (e.g., predictive analytics, GenAI copilots, automation) to accelerate revenue, efficiency, customer experience, and risk management. The ideal candidate is equally fluent in business strategy and applied AI, with a track record of delivering outcomes at scale and building high\-performing, cross\-functional teams.
What You’ll Do (Key Responsibilities)
Enterprise Strategy
- Lead the annual and multi\-year strategic planning process; define the company’s strategic agenda, growth bets, and portfolio priorities aligned to CTO/C\-Suite direction.
- Build market/competitive intelligence and scenario planning to inform capital allocation and M\&A/partnership decisions.
- Translate strategy into OKRs and measurable roadmaps; partner with Finance and Business Unit (BU) leaders on targets and accountability.
AI \& Data Leadership
- Create and execute the enterprise AI strategy (classical ML \+ GenAI), including use\-case prioritization, value sizing, risk assessment, and adoption plans.
- Stand up or mature AI operating model: governance, model lifecycle management (ML Ops), prompt and model risk management, and responsible AI policies (privacy, security, fairness, transparency).
- Drive delivery of high\-value AI/automation products (e.g., demand forecasting, churn prediction, intelligent decision support, copilots for sales/service, content generation, document intelligence).
- Partner with CEO/CTO/CRO and Data leaders to ensure enabling platforms: data quality, metadata, feature stores, vector DBs, model registries, observability, and cost governance.
Business Outcomes \& Change
- Own value realization: quantify and report ROI, productivity gains, CX improvements, and risk reduction linked to AI and strategic initiatives.
- Champion change management and workforce enablement (training, adoption, new ways of working), in close partnership with HR/L\&D.
- Establish a culture of experimentation and continuous improvement (A/B testing, pilots, phased scale).
Stakeholder \& People Leadership
- Advise CTO/C\-Suite on strategy and AI opportunities/risks; prepare executive materials and communicate progress with clarity.
- Build and lead a diverse Strategy \& AI organization (strategy, product, data science/ML, analytics translators, program management).
- Manage a portfolio of internal and external partnerships (hyperscalers, ISVs, SIs, startups, academic institutions).
Required Qualifications
- 10\+ years in strategy, corporate development, digital/AI transformation, or related roles, with 5\+ years leading teams and enterprise programs.
- Demonstrated experience shipping AI/ML or analytics products that drove measurable business impact (revenue growth, cost reduction, risk mitigation, or CX improvement).
- Strong command of strategic analysis (market sizing, unit economics, portfolio strategy) and AI concepts (supervised/unsupervised learning, GenAI, RAG, evaluation, model risk).
- Proven ability to influence C\-suite, align cross\-functional stakeholders, and lead through ambiguity.
- Excellent communication skills; able to translate technical topics into business outcomes.
Preferred Qualifications
- Experience in financial services
- Prior ownership of an AI program with governance at scale (e.g., \>10 use cases, model monitoring, policy/compliance alignment).
- Familiarity with major cloud ecosystems (Azure/AWS/GCP), data platforms (lakehouse), MLOps tooling, and modern product delivery (Agile, product ops).
- Advanced degree in Business, Computer Science, Data Science, Engineering, or related field (MBA or MS/PhD a plus).
- Exposure to regulatory environments affecting data/AI (e.g., privacy, model governance, sector\-specific regs).
Leadership Competencies
- Strategic Agility: Anticipates shifts, frames choices, and allocates resources to the highest\-value bets.
- Outcome Orientation: Sets clear OKRs, tracks ROI, and course\-corrects quickly.
- Technical Depth \+ Translation: Credible with technical teams and business leaders; “bilingual” in AI and P\&L.
- Inclusive Leadership: Builds diverse, psychologically safe teams; develops talent and succession.
- Change Leadership: Inspires adoption and operationalizes new ways of working at scale.
Success Metrics (First 12–18 Months)
- Clear 3\-year strategy and investment thesis approved by CTO/C\-Suite.
- AI portfolio defined and sequenced; 3 productionized AI products delivering quantified value.
- Responsible AI governance and model lifecycle controls operational and audited.
- Uplift in targeted KPIs (choose relevant to client): revenue growth %, margin improvement bps, cycle\-time reduction, NPS/CSAT increase, compliance findings reduced, cost\-to\-serve decrease.
- Organization health: hiring critical roles, engagement scores, training completion/adoption metrics.
Compensation \& Benefits
- Compensation: Competitive base salary \+ bonus
- Benefits: Medical, dental, vision, 401(k) with company match, paid time off, parental leave, wellness programs, and other executive benefits.
EEO \& Workplace Practices
GRT is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other protected characteristic. Reasonable accommodations are available upon request.
Salary Context
This $150K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At GRT Financial, Inc, 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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($162K) sits 9% below the category median. Disclosed range: $150K to $175K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
GRT Financial, Inc AI Hiring
GRT Financial, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Southfield, MI, US. Compensation range: $175K - $175K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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