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About This Role
Client Executive Director – Data \& AI Consulting Sales
United States – Remote \| Midwest Preferred
Company Overview
Argano is a global digital consultancy focused on high\-performance business operations. We help organizations transform how they operate by connecting strategy, technology, data, and execution to improve agility, profitability, customer experience, and growth.
Our Connect Business Unit brings together expertise across Data, Analytics, Artificial Intelligence, Cloud, Enterprise Integration, and Custom Applications to help clients modernize their technology environments and turn data into measurable business outcomes.
Job Summary:
Argano is seeking a Client Executive Director – Data \& AI Consulting Sales, to drive new business growth across the Midwest.
This is a senior, individual\-contributor sales role designed for an accomplished hunter who thrives on creating opportunities rather than inheriting established accounts. You will be responsible for identifying prospective clients, opening new relationships, originating opportunities, and leading complex consulting sales engagements from initial outreach through contract execution and close.
The ideal candidate combines strong industry expertise with experience selling consulting and transformation services across Data, Analytics, AI, modern data platforms, and Enterprise Integration.
You should be comfortable engaging senior business and technology executives around how AI, machine learning, analytics, and modern data architectures can solve meaningful operational challenges—from predictive forecasting and supply chain visibility to Smart Factory and Industry 4\.0 initiatives.
Success in this role requires someone who is entrepreneurial, highly self\-motivated, comfortable creating pipeline from the ground up, and willing to personally do the work required to win new business, including prospecting, networking, attending industry events, developing executive relationships, and driving opportunities through a signed SOW.
Key Responsibilities:
- Drive net\-new consulting services revenue with a primary focus on the Midwest.
- Build pipeline from the ground up through proactive prospecting, executive networking, referrals, industry events, ecosystem relationships, and targeted account development.
- Originate opportunities and personally manage the full sales lifecycle, from initial prospecting and discovery through solution development, proposal, negotiation, SOW execution, and close.
- Position Argano's capabilities across Data, Analytics, AI, Enterprise Integration, Cloud, and Custom Applications against clients' most important business and operational priorities.
- Engage senior executives and decision\-makers as a trusted advisor, connecting technology investments to measurable business outcomes.
- Develop compelling value propositions around modern data platforms, advanced analytics, artificial intelligence, machine learning, and enterprise integration.
- Help manufacturing and supply chain clients identify opportunities to leverage AI and data for predictive forecasting, demand planning, inventory optimization, supply chain visibility, predictive fulfillment, and operational performance.
- Develop opportunities related to Smart Factory, Industry 4\.0, predictive analytics, and AI\-enabled manufacturing transformation.
- Partner with Argano solution architects, technical leaders, delivery teams, and ecosystem partners to shape solutions and proposals that address complex client requirements.
- Build relationships across relevant technology ecosystems, including platforms such as Databricks, Snowflake, AWS, GCP, MuleSoft, and Boomi.
- Develop and execute strategic territory and account plans designed to consistently achieve or exceed bookings and revenue targets.
- Maintain ownership and momentum throughout complex consulting sales cycles involving multiple business and technical stakeholders.
- Represent Argano in the market through customer meetings, partner activities, conferences, industry events, and other business\-development opportunities.
- Travel as necessary to build relationships, develop opportunities, and advance strategic sales pursuits.
Minimum and Preferred Qualifications
Experience:
- 8\+ years of technology consulting or professional services sales experience, with demonstrated success selling complex transformation engagements.
- Proven track record as a net\-new business hunter who has independently generated pipeline and converted opportunities into signed consulting engagements.
- Demonstrated history of meeting or exceeding sales, bookings, or revenue targets.
- Experience personally originating opportunities and managing the complete deal lifecycle through signed SOW and close.
- Strong experience selling consulting services involving Data, Analytics, Artificial Intelligence, modern data platforms, Cloud, and/or Enterprise Integration.
- Experience selling into manufacturing and supply chain organizations strongly preferred.
- Experience with discrete and/or industrial manufacturing is particularly relevant.
- Additional industry experience across Retail, Consumer Products/CPG, Life Sciences, or Healthcare is valuable.
- Demonstrated ability to develop relationships with senior business and technology executives and translate complex technology capabilities into business value.
- Experience operating within complex consulting sales environments and collaborating with solution, delivery, technical, and partner teams.
Technology \& Solution Knowledge:
Candidates should possess strong commercial knowledge of Data \& AI solutions and be capable of leading credible executive conversations around areas such as:
- Modern data platforms and architectures
- Data modernization and transformation
- Advanced analytics
- Artificial Intelligence and Machine Learning
- Predictive analytics
- Data integration
- Enterprise integration
- Cloud\-based Data \& AI solutions
- Smart Factory and Industry 4\.0
- AI\-enabled supply chain and manufacturing transformation
Experience selling consulting services involving one or more of the following ecosystems is highly desirable:
- Databricks
- Snowflake
- AWS Data \& AI
- Google Cloud Platform (GCP) Data \& AI
- MuleSoft
- Boomi
Candidates do not need experience across every platform. More important is the ability to understand how modern data, analytics, AI, and integration technologies come together to solve complex business problems and to successfully sell the consulting services required to deliver those outcomes.
Manufacturing \& Supply Chain Expertise:
The strongest candidates will understand how Data \& AI can address operational challenges within manufacturing and supply chain environments, including:
- Predictive demand forecasting
- Predictive supply chain analytics
- Inventory visibility and optimization
- Predictive fulfillment
- Production and operational analytics
- Smart Factory initiatives
- Industry 4\.0 transformation
- AI and machine learning applications
- Supply chain visibility
- Pricing and demand analytics
- Data\-driven operational decision\-making
The ability to engage executives around these business outcomes—not simply discuss technology features—is critical.
What Success Looks Like:
Success in this role requires more than participating in opportunities created by others. We are looking for a seller who can demonstrate a history of creating opportunities, building executive relationships, developing solutions, navigating complex buying organizations, negotiating commercial terms, and personally driving engagements through close.
You should be someone who:
- Operates with a strong hunter mentality.
- Is comfortable building a territory and pipeline rather than relying on established accounts.
- Takes personal ownership of lead generation and business development.
- Has the persistence and discipline required to develop new relationships over time.
- Can articulate sophisticated Data \& AI capabilities in terms of business outcomes.
- Demonstrates executive presence and credibility with senior client stakeholders.
- Works effectively across technical, delivery, alliance, and consulting teams.
- Is comfortable traveling and meeting clients and partners in person.
- Has demonstrated career stability and a history of staying long enough within organizations to produce measurable results.
Education:
Bachelor's degree in Business, Information Technology, Computer Science, Engineering, or a related field preferred. An MBA or other relevant advanced degree is a plus.
Location:
This is a U.S.\-based remote position with a strong preference for candidates located in the Midwest, including markets such as Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin.
Travel to clients, industry events, partner meetings, and other business\-development activities will be required.
Why Argano:
This is an opportunity for an entrepreneurial sales executive to build business at the intersection of some of the most important areas of enterprise transformation: Data, AI, Analytics, Integration, Cloud, and intelligent operations.
Rather than simply selling a technology platform, you will help executives determine how modern data and AI capabilities can transform critical business operations—from manufacturing and supply chain performance to forecasting, fulfillment, and intelligent decision\-making.
You will have the opportunity to create new client relationships, shape complex transformation opportunities, collaborate with experienced technology and consulting leaders, and directly influence the growth of Argano's Connect business.
About Argano:
Argano is a digital consultancy immersed in high\-performance operations. We help enterprises navigate evolving markets by combining transformative strategies and technologies to improve customer experiences, unlock commercial innovation, increase operational efficiency, and drive sustainable growth.
Argano is an equal\-opportunity employer. All applicants will be considered for employment without regard to race, color, religion, sex
Argano is the first of its kind: a digital consultancy totally immersed in high\-performance operations. We steward enterprises through ever\-evolving markets, empowering them with transformative strategies and technologies to exceed customer expectations, unlock commercial innovation, and drive optimal efficiency and growth.
Argano is an equal\-opportunity employer. All applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability status.
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 Argano, 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.
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.
Argano AI Hiring
Argano has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 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 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.
Frequently Asked Questions
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