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About This Role
Solution Director: Data & AI
Location: East Coast (NY/NJ, Atlanta, Charlotte, or South Florida)
Who We Are
AND Digital is a tech company dedicated to accelerating digital delivery and closing the digital skills gap. Since 2014, we have supported organisations in building better digital products and stronger digital teams.
We believe our work should always create a remarkable impact for our clients. Through our regional offices, known as ‘Clubs,’ we build strong relationships with our partners, ensuring they are prioritised by teams located nearby.
This unique model has driven success for both our clients and ourselves, reflected in our remarkable organic growth since 2014. Today, we are over 1,300 people strong, with Clubs across the UK, Europe, and the USA—and plans for global expansion in the coming years.
Join us and help fulfil our mission to close the world’s digital skills gap.
Solution Director – Data & AI As a Technical Solution Director within our central Practice Area, you will play a pivotal role in shaping and driving the growth of AND Digital’s Data & AI business. Reporting directly to the US CTO, you’ll define and execute our go-to-market strategy, build strong ecosystem partnerships, and guide solution delivery through AND’s unique club model. Acting as a senior advisor to clients, you will design and lead the delivery of transformative enterprise-grade Data & AI solutions, leveraging deep expertise across AWS, Snowflake, and other modern data platforms.
As part of a rapidly scaling US operation—growing over 400% year-over-year—you’ll collaborate with global powerhouses and C-Suite leaders across industries such as Financial Services, Travel, and beyond. In addition to driving client success, you’ll help shape AND Digital’s AI capability across North America, fostering technical excellence and growing internal capability to meet the expanding demand for intelligent data-driven transformation.
What You’ll Do
- Engage with clients
- Act as a trusted advisor to C-suite stakeholders (CTO/CDO/CPO).
- Build relationships that uncover client challenges and opportunities.
- Show the art of the possible in Data & AI, connecting innovation to measurable outcomes.
- Shape solutions
- Translate complex business needs into compelling, value-driven Data & AI solutions.
- Guide teams to deliver against vision and ensure robust, outcome-focused delivery.
- Support business case development and pre-sales.
- Technical & Delivery Oversight
- Client-facing AI architecture, technology-agnostic solutioning (AWS, Google, Microsoft) and technical delivery oversight - The gate keeper for all things AI.
- Oversee architecture quality and delivery outcomes across multiple engagements.
- Ensure projects align to measurable client KPIs and business results.
- Drive automation, MLOps, and engineering best practices through coaching and governance.
- Drive growth
- Identify, qualify, and shape new opportunities across AND.
- Partner with Client Teams to develop deals, coach others, and influence outcomes.
- Collaborate closely with partners (e.g. AWS) to co-sell and co-market solutions.
- Help define go-to-market strategy and reusable accelerators for AI and data offerings.
- Represent AND Digital’s AI capability in the market externally, contributing to proposals, events, and industry forums.
- Achieve a leveraged revenue impact target of around $2.5m+.
- Lead our propositions and Practice IP
- Own and evolve AND’s Data & AI proposition, partnerships, and thought leadership.
- Be a visible leader in the Data & AI community—driving consistency, quality, and innovation.
- Develop training and mentoring to grow internal capability.
- Lead and inspire
- Provide senior oversight, coaching, and technical challenge across engagements.
- Champion excellence and act as a role model for proactive, positive leadership.
- Mentor AI and data engineers across the US and nearshore teams.
- Shape the foundation of AND Digital’s AI practice in the US.
- Contribute to US recruitment, onboarding, and capability growth across AI, Data and Platform Engineering, as well as more broadly.
Key Evaluation Criteria
- Minimum 2+ years in recent leadership roles within AI OR a hands-on, client facing individual who is looking for a step up.
- Has led teams shipping AI-driven software into production with measurable impact.
- Balances deep technical fluency with strong client-facing and commercial ability.
- Demonstrated experience embedding AI into client products or workflows.
- Has worked with hyperscale clientele and/or consultancies.
- Experienced in architecture reviews, code quality, and system design governance.
- Proven success in consulting or digital delivery environments, ideally bridging product and services.
- Comfortable in fast-paced, outcome-based commercial models (fixed-price and outcome-driven).
- Experienced in coaching, scaling, and enabling teams across multiple geographies.
- Uses metrics or OKRs to tie delivery to business outcomes.
Experience & Skills
- 10 to 15 years in technology or consulting, with 6+ years in Data and AI delivery.
- Proven record of leading enterprise AI or data programs for Fortune 1000 or mid-market clients.
- Strong technical background in AWS, Snowflake, and other modern data platforms.
- + For example, able to lead solution architecture and delivery across AWS AI technologies - including Bedrock, SageMaker, and Connect, whilst maintaining a technology-agnostic approach when required.
- Strong commercial and stakeholder management skills.
- Excellent communication and storytelling ability - credible with both engineers and executives.
- Must reside on the East Coast (NY/NJ, Atlanta, or South Florida).
Why join AND Digital?
We have three core values: wonder, share, and delight. These values inform how we work with clients, and our culture: what it feels like to work for AND. We believe collaboration, ambition, curiosity and fun can drive innovation by creating a better environment for problem-solving.
By joining AND, we’ll provide:
- Opportunities to work on projects with big clients and produce meaningful work that makes a genuine difference to people’s lives.
- A “Blended Working” model, meaning you will be able to work in a range of locations from; your home, in your clubhouse, on a client, as well as just a change of scenery.
- The benefits of having a small company feel through being part of an autonomous Club, while being able to take in the benefits of a larger organisation.
- A dedicated career scrum team, designed to help you reach your career goals and develop the skills you need to be your best self.
- A safe environment for you to be yourself and challenge yourself.
Equal Opportunities Statement
At AND Digital we embrace diversity and are committed to equal opportunities. We are actively recruiting for a diverse and inclusive workforce so want to ensure we do everything we can to support your application.
We want you to feel safe and empowered to let us know if you require any adjustments to be made to your application or interview process so please speak to our recruitment team.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At And Digital, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
And Digital AI Hiring
And Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jersey City, NJ, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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