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About Us:
About Stagwell
Stagwell is a global challenger network built to transform marketing through technology, creativity, data, and artificial intelligence.
Enterprise AI Solutions is a strategic growth initiative focused on helping organizations deploy and operationalize AI\-powered solutions across marketing, customer experience, data, analytics, and business operations.
As organizations move from AI experimentation to enterprise\-wide adoption, Stagwell helps customers integrate data, technology, workflows, and AI capabilities into scalable operating models that deliver measurable business impact.
Overview:
Position Overview
We are seeking a highly technical, customer\-facing AI Forward Deployed Engineer to work directly with organizations as they deploy and scale AI\-powered solutions.
The AI Forward Deployed Engineer serves as a forward\-deployed partner to enterprise customers, working directly with stakeholders to understand business challenges, design practical AI solutions, drive implementation, and ensure successful operational adoption.
The ideal candidate combines technical expertise, solution architecture skills, business acumen, and a builder mentality. They enjoy solving complex problems, navigating ambiguity, and working directly with customers to turn AI concepts into deployed solutions that drive measurable outcomes.
Success in this role requires a strong sense of ownership and a willingness to move beyond recommendations into execution. This is a highly hands\-on individual contributor role that works closely with enterprise customers, internal sales teams, product teams, and delivery teams to help customers become successful with AI. The initial focus of this role will be supporting Stagwell's enterprise AI application suite, including Stagwell Agentic Targeting System (SATS) Powered by Palantir and Stagwell Pulse Powered by Palantir, working closely with customers to drive successful deployment, adoption, and business outcomes.
Responsibilities:
ResponsibilitiesPartner With Customers and Sales Teams
- Work directly with enterprise customers to understand business objectives, operational challenges, data environments, and AI transformation priorities.
- Partner closely with sales teams throughout the customer lifecycle, from discovery and demos through deployment and adoption.
- Support customer meetings, technical discovery sessions, solution workshops, and executive presentations.
- Help sales teams tell the AI story through customer\-specific scenarios, demonstrations, and solution positioning.
- Serve as a trusted advisor to both customer stakeholders and internal go\-to\-market teams.
- Support customer onboarding, adoption efforts, and ongoing technical success initiatives.
Design \& Deploy AI Solutions
- Architect practical AI\-powered solutions that address real business challenges.
- Translate customer requirements into deployable solution designs.
- Build customer\-specific use cases, workflows, and AI\-powered business scenarios.
- Develop and validate proof\-of\-concepts, pilot programs, and production\-ready solutions.
- Configure, optimize, and operationalize AI solutions within customer environments.
- Partner with technical teams to accelerate implementation and adoption.
- Help customers establish repeatable AI\-enabled workflows and operating models.
Lead Enterprise AI Deployments
- Own implementation efforts from discovery through deployment.
- Facilitate technical workshops, solution working sessions, and implementation planning.
- Support customer onboarding, technical validation, testing, and rollout activities.
- Navigate technical, operational, and organizational obstacles to ensure successful execution.
- Drive cross\-functional alignment between business stakeholders, technical teams, and implementation partners.
- Ensure successful transition from pilot programs to production deployments.
Work With Customer Data and Business Processes
- Translate customer business requirements into data models, workflows, and AI\-enabled use cases.
- Help map customer processes, data flows, audience structures, and operational requirements.
- Work through customer data requests, integrations, and implementation considerations.
- Support Palantir Ontology development, workflow design, and solution mapping activities.
- Identify opportunities to improve customer workflows through AI, automation, analytics, and data\-driven decision making.
- Collaborate with customer teams to operationalize solutions within existing business processes.
Solve Complex Problems
- Work directly with customer stakeholders to identify opportunities where AI can create measurable business value.
- Rapidly evaluate new use cases and determine practical implementation approaches.
- Troubleshoot solution gaps, workflow challenges, and adoption barriers.
- Iterate quickly based on customer feedback and evolving business requirements.
- Help customers move from experimentation to enterprise\-scale adoption.
- Balance technical constraints, business priorities, and customer goals to drive successful outcomes.
Shape the Future of Enterprise AI Solutions
- Surface emerging customer needs, market trends, and solution opportunities.
- Contribute to reusable frameworks, playbooks, demo environments, and implementation methodologies.
- Influence product strategy and roadmap decisions through direct customer engagement.
- Help establish best practices for enterprise AI deployment across Stagwell.
- Support the development of repeatable deployment and onboarding programs.
- Share learnings that improve how Enterprise AI Solutions engages customers and scales delivery.
What Success Looks Like
- Enterprise AI solutions successfully deployed within customer environments.
- Customers achieve measurable operational and business outcomes through AI adoption.
- AI use cases move from proof\-of\-concept to production implementation.
- Sales teams are enabled with stronger demonstrations, customer scenarios, and technical support.
- Customer data requirements and business processes are successfully translated into working solutions.
- Strong executive and practitioner relationships are established across customer organizations.
- Repeatable deployment methodologies and implementation frameworks are created and scaled.
- Stagwell becomes a trusted partner for organizations navigating enterprise AI transformation.
Qualifications:
Required Qualifications
- 5\+ years of experience in Solutions Architecture, Solutions Engineering, Technical Consulting, Implementation Consulting, Sales Engineering, AI Consulting, Enterprise Software, Digital Transformation, or similar customer\-facing technical roles.
- Experience working directly with enterprise customers in complex technology environments.
- Proven ability to translate business challenges into deployed technology solutions.
- Experience leading discovery sessions, workshops, solution design efforts, onboarding activities, and implementation initiatives.
- Hands\-on experience supporting customer deployments, pilots, proof\-of\-concepts, technical evaluations, or production rollouts.
- Strong understanding of enterprise data ecosystems, customer data platforms, analytics platforms, identity resolution, audience intelligence, and modern marketing technology environments.
- Experience working with customer workflows, business processes, data mapping, or solution configuration activities.
- Ability to communicate effectively with both executive and technical stakeholders.
- Strong problem\-solving skills and comfort operating within ambiguous, evolving environments.
- Demonstrated ownership mentality and ability to drive initiatives from concept through execution.
Preferred Qualifications
- Experience with AI, machine learning, customer intelligence, marketing technology, customer data platforms, analytics, or enterprise software ecosystems. Familiarity with marketing technology workflows and processes is strongly preferred.
- Experience supporting technical sales motions, customer demonstrations, onboarding programs, and solution adoption efforts.
- Familiarity with Palantir Ontology concepts, customer data models, workflow mapping, audience activation, marketing measurement, and enterprise data architectures.
- Experience supporting Fortune 1000 organizations and large\-scale transformation initiatives.
- Understanding of generative AI, agentic AI systems, intelligent automation, and enterprise AI deployment models.
- Experience working with marketing, media, customer experience, analytics, or growth teams.
Compensation:
In order to comply with equal pay and salary transparency laws in various locations, we believe the target range of base compensation in New York City for this role is $160,000 \- $200,000\. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and location. This role includes commission\-based incentive compensation.
In addition to medical, dental and vision coverage, we offer a generous PTO plan, 401k program, comprehensive family planning benefits (including paid parental leave), tuition reimbursement, and pre\-tax commuter benefits. Benefits/perks may vary depending on the nature of your employment with Stagwell and the location where you work.
Salary Context
This $160K-$200K 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 Stagwell Global, LLC, 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $160K to $200K.
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.
Stagwell Global, LLC AI Hiring
Stagwell Global, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $200K - $200K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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