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About This Role
Director, AI and Technology and Enablement About TMG
The Miles Group (TMG) is a leadership advisory firm dedicated to developing exceptional talent – from the C\-suite to the next generation of leaders – at some of the world’s most prestigious companies. Our clients trust us as truly independent, unbiased advisors, tailoring every engagement to their unique culture and context. We serve organizations through executive coaching, succession, executive assessment, board advisory, and strategic team offsites. About the Role
TMG is investing in the systems, workflows, and AI\-enabled tools that will allow us to operate at a higher level with greater visibility, efficiency, and consistency across business development, client service, and firm operations. The Director, AI and Technology Enablement, will play a critical role in the execution of that effort.
The role will focus on Salesforce enhancements, AI\-enabled workflows, growth systems, and the data and process infrastructure that connects them. Day\-to\-day that means translating business needs into clear requirements, workflows, testing plans, and adoption materials – and working with relevant internal and external partners to build and drive adoption and use.
This is not a pure technology role. It is a role for someone who understands how a high\-touch, relationship\-driven professional services firm works and who can bridge effectively between business stakeholders and technical partners. You will report to the VP of Growth Operations and Enterprise Systems and work closely with the Head of Growth, the Head of Client Strategy, IT, shared services, and third\-party vendors. The connective tissue matters as much as the execution.
You are systems\-oriented, organized, and genuinely curious about AI and technology – and know that in an environment like TMG, tools only work if they are useful, intuitive, and grounded in how people work.
Key Responsibilities
Business Technology Execution* Translate growth, client\-service, and operating priorities into clear technology requirements, workflows, user needs, and implementation plans.
- Partner with the VP of Growth Operations and Enterprise Systems to sequence technology\-enabled workstreams and ensure progress against agreed priorities.
- Serve as the connective tissue between business stakeholders, IT, shared services, and third\-party partners.
- Ensure technology work is grounded in practical use cases that improve how TMG operates, supports growth, and serves clients.
- Track open items, decisions, dependencies, testing needs, and rollout requirements across technology\-enabled initiatives.
Salesforce Workflow and Data Logic* Support Salesforce enablement tied to growth, account visibility, relationship intelligence, resource planning, and reporting.
- Define business requirements for fields, workflows, data capture, dashboards, reporting inputs, and user experience improvement.
- Partner with internal users and external vendors to identify pain points, adoption barriers, and opportunities to improve usability.
- Support data\-quality efforts by clarifying what information needs to be captured, by whom, and how it will be used.
- Ensure Salesforce workflows connect to the firm’s broader operating cadences, reporting needs, and decision\-making rhythms.
AI Tools and Tech\-Enabled Workflows* Support the adoption of AI\-enabled tools across business development, knowledge management, account intelligence, reporting, and client\-service workflows.
- Identify and prioritize AI use cases that create real, practical, leverage for the business.
- Translate AI opportunities into clear workflows, prompts, testing plans, feedback loops, and adoption materials.
- Partner with internal stakeholders and external partners to test, refine, and roll out AI\-enabled capabilities.
- Maintain a practical focus on usability, quality, confidentiality, and business impact.
Vendor Coordination and Implementation Support* Coordination with third\-party partners on requirements, timeliness, testing, feedback, issue solution, and implementation and follow\-through.
- Prepare vendors for business conversations by providing clear context, requirements, and priorities.
- Track vendor deliverables, open questions, dependencies, and next steps; escalate risks or execution concerns proactively.
- Support testing and user feedback processes to ensure enhancements are practical and aligned with business needs.
Adoption, Training, and User Support* Develop user guidance, training materials, workflow documentation, and adoption support for new or enhanced tools.
- Support business users in understanding how to use Salesforce, AI tools, and related systems in ways that drive growth, client service, and reporting.
- Gather user feedback; identify friction points and opportunities for improvement.
- Support rollout planning so new tools and workflows are introduced thoughtfully and with appropriate change\-management support.
- Help reinforce consistent use of systems without creating unnecessary complexity.
Data, Workflow, and Process Improvement* Map current\-state workflows and identify opportunities to reduce manual effort, duplication, or unclear ownership.
- Connect data logic across Salesforce, reporting tools, AI\-enabled workflows, and knowledge\-management system.
- Support improvements that make information easier to capture, find, report, and use.
- Partner with the VP of Growth Operations and Enterprise Systems and relevant stakeholders to ensure data and workflow improvements support accounting planning, BD enablement, reporting, and leadership visibility.
- Bring a continuous improvement mindset to how growth is used, maintained, and evolved.
Experience and Qualifications* 8\-10 years of experience in business systems, technology enablement, operations, revenue operations, CRM operations, AI workflow enablement, consulting operations, or related fields.
- Bachelor’s degree or equivalent experience required.
- Proven track record of working with Salesforce, including workflow improvement, reporting inputs, adoption support, or business requirements gathering.
- Demonstrated ability to translate business needs into requirements for technical teams or third\-party partners.
- Demonstrated success in supporting technology implementation, system enhancements, user testing, training, or adoption efforts.
- Exposure to AI\-enabled tools, automation, knowledge\-management systems, or workflow improvement tools strongly preferred.
- Experience in professional services, consulting, executive search, leadership advisory, or another relationship\-driven business required.
- Proven ability to collaborate with senior stakeholders, business users, vendors, and technical partners.
Capabilities and Characteristics* Systems\-oriented; naturally see how tools, data, workflows, and user behavior connect and where the gaps are.
- Translate business needs into clear implementation requirements without over\-engineering the solution.
- Disciplined project management: track workstreams, dependencies, timelines, and follow\-though across multiple priorities without losing momentum.
- Sound judgment around usability, adoption, confidentiality, and business impact.
- Effective communication skills; able to explain technical or workflow topics clearly to non\-technical users.
- Curious and forward\-looking AI, technology\-enabled workflows, and how tools can improve professional\-services work.
- Collaborative and service\-oriented; earn trust across senior leaders, analysts, IT, shared services, and external vendors.
- Thrive in an environment where the path is not fully defined and help bring structure to ambiguity rather than waiting for it.
- Practical and solutions\-oriented; focused on tools and workflows that work easier, clearer, and more effectively.
Why TMG
This is an opportunity to help shape how a highly regarded leadership advisory firm uses technology, AI, and systems to support its next stage of growth. You will work closely with business leaders, operators, and external vendors to turn growth and technology priorities into practical workflows that improve visibility, efficiency, knowledge\-sharing, and client\-service support across the firm. The work requires curiosity, judgment, organization, and follow\-through along with an appreciation for the high\-touch, confidential, and relationship\-driven nature of TMG’s work.
Compensation
TMG offers a competitive base salary, discretionary bonus, and benefits package. A reasonable estimate of the base salary for this position is $160,000 \- $210,000, with the potential for additional performance\-based earnings. Actual compensation within the range will be dependent upon the individual’s skills, experience, qualifications, location, and applicable employment laws. Salary is one component of TMG’s total rewards package, which includes a discretionary performance bonus, 401K, generous paid time off, benefits, and wellness program.
Equal Employment Opportunity
TMG provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, TMG complies with all applicable state and local laws governing nondiscrimination in employment in every location in which the company operates. This policy applies to all terms and conditions of employment including recruiting, hiring, placement, promotion, termination, recall, transfer, leaves of absence, compensation, and training.*TMG does not accept unsolicited candidates, referrals, or resumes from any staffing agency, recruiting service, sourcing entity, or third\-party paid service at any time. Any referrals, resumes, or candidates submitted to TMG or any employee or owner of TMG without a pre\-existing agreement signed by both parties covering the submission with be considered TMG’s property and not subject to any fees or charges. For existing agreements, a role must be approved and open to external search; otherwise, unsolicited and unapproved submittals and referrals will be considered TMG's property and free of fees.*685 Third Avenue, 22nd Floor \| New York, NY 10017 \| P 212\.899\.6928 F 212\.332\.3791 \| miles\-group.com
Salary Context
This $160K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At The Miles Group, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $160K to $210K.
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.
The Miles Group AI Hiring
The Miles Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $210K - $210K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 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).
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 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.
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