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Overview:
CVP is seeking a commercially trained Federal AI Sales Executive to create and close new business for CVP’s Fifer artificial intelligence platform and products. Reporting to the Chief Growth Officer and working closely with the Chief Executive Officer and Chief AI Officer, this individual will own the sales process for Fifer: identifying priority customers, creating customer access, securing first meetings, building customer interest, qualifying opportunities, and advancing them through pilot, procurement, award, and expansion.
This is a product\-oriented enterprise sales role. The successful candidate must be able to open doors, command the first customer conversation, establish Fifer’s relevance to the customer’s mission, and remain commercially accountable through close. The sales approach will be relationship\-based and highly targeted rather than dependent on high\-volume cold calling. It will combine executive relationship development, sophisticated discovery, consultative selling, and customer co\-creation.
Responsibilities:
Develop and execute a focused federal target\-account strategy for Fifer, prioritizing agencies and programs with strong mission fit, relationship pathways, funding potential, and product relevance.* Create access to federal executives, mission leaders, CIO and CISO organizations, chief data and AI leaders, program offices, and acquisition stakeholders through established relationships, referrals, partners, industry networks, events, and tailored executive outreach.
- Secure and lead initial customer meetings, clearly articulating what Fifer is, the problems it solves, and how it differs from generic AI tools or labor\-intensive consulting approaches.
- Conduct disciplined discovery to uncover the customer’s mission problem, affected users and workflows, measurable impact, urgency, current alternatives, decision criteria, technical constraints, and path to purchase.
- Build and maintain a qualified pipeline of new Fifer opportunities and apply rigorous qualification based on customer need, executive sponsorship, champion strength, funding, acquisition path, technical fit, competition, and timing.
- Shape and advance opportunities from first conversation through demonstrations, workshops, pilots, proofs of value, market research, proposals, procurement, negotiation, and award.
- Remain commercially accountable throughout the pursuit while orchestrating the right CVP product, technical, capture, contracts, delivery, and executive resources at each stage.
- Lead customer co\-creation sessions that translate mission challenges into practical Fifer use cases, measurable outcomes, pilot concepts, and adoption roadmaps without sacrificing product scalability.
- Maintain accurate CRM records, account plans, opportunity strategies, forecasts, and next\-action commitments, and provide transparent reporting to the Chief Growth Officer.
- Capture market and customer feedback that informs Fifer product strategy, packaging, pricing, messaging, demonstrations, and federal sales plays.
Attributes for Success
We are looking for a hungry athlete to hit the ground running. The successful candidate will have a hunter\-oriented outlook with a creative approach to breaking down barriers to customer access and closing the sale. They will create urgency without sacrificing credibility, moving opportunities toward clear customer commitments, and operate as the commercial quarterback for a multidisciplinary CVP pursuit team.
Qualifications:
- Eight or more years of progressive experience in enterprise technology sales, or product\-focused account development, with federal experience as an added bonus
- Demonstrated record of business development and pipeline generation activities such as creating new customer access, securing executive meetings, and building qualified pipeline
- A history of consistently exceeding sales quotas
- Experience selling enterprise software, AI, data and analytics, automation, cybersecurity, cloud, digital transformation, or another complex technology offering.
- Experience converting pilots or proofs of value into production deployments, broader product adoption, and recurring revenue.
- Ability to independently lead the first customer meeting, establish executive\-level relevance, conduct sophisticated discovery, handle early objections, and earn the next customer commitment.
- Technical fluency sufficient to discuss AI platforms, data, workflow automation, cloud, integration, security, governance, and implementation considerations without serving as the detailed solution architect.
- Exceptional executive presence, communication, listening, questioning, presentation, negotiation, and relationship\-management skills.
- Ability to succeed in a consulting and technology company where product, engineering, delivery, capture, contracts, and executive teams must operate as one pursuit team.
Preferred Qualifications* Established relationships within federal civilian health, public health, human services, national security, defense, or other markets aligned with CVP’s growth priorities.
- Strong knowledge of government procurement practices and pathways, including: buying dynamics, budgeting, acquisition stakeholders, contract vehicles, market research, pilots, task\-order competitions, proposals, and long\-cycle decision processes.
- Experience selling AI platforms, generative AI, intelligent automation, analytics, enterprise software, or emerging technology in highly regulated environments.
- Formal sales training and experience using a rigorous enterprise sales methodology for discovery, qualification, account planning, opportunity progression, and forecasting (experience with MEDDPICC or similar sales methodologies).
- Experience in a growth\-stage technology company, consulting firm with proprietary products, systems integrator, or software company where the seller must create market demand.
- Familiarity with responsible AI, federal cybersecurity and privacy requirements, data governance, FedRAMP, authority\-to\-operate considerations, and government technology acquisition.Measures of SuccessMeasures of Success
- Quality and value of self\-sourced and influenced Fifer pipeline, with strong forecasting hygiene and discipline
- Strategic first meetings secured within priority federal accounts.
- Conversion of initial meetings into qualified discovery, pilots, procurements, awards, and expansion opportunities.
- Achievement of agreed bookings, revenue, new\-customer bookings, and pipeline objectives.
- Strength of executive sponsors and customer champions
About CVP
CVP is an award\-winning healthcare and next\-gen technology and consulting services firm solving critical problems for healthcare, national security, and public sector clients. We help organizations achieve lasting transformation.
CVP is an Equal Opportunity Employer dedicated to actively recruiting individuals and providing advancement opportunities based on merit and legitimate job qualifications. We ensure that all associates receive equal opportunities based on their personal qualifications and job requirements. CVP strictly prohibits any form of discrimination or harassment.
At CVP, we cultivate a work environment that encourages fairness, teamwork, and respect among all associated. We are committed to maintaining a workplace where everyone can grow both personally and professionally.
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 Customer Value Partners (CVP), 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Customer Value Partners (CVP) AI Hiring
Customer Value Partners (CVP) 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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