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About This Role
About This Role 12 to 20 years of experience
Skills: Product & Program Ownership, Executive Communication & Governance, Growth Strategy & Business Impact, Go-to-Market Alignment & Adoption, Strategic Advisory, Team Leadership & Stakeholder Partnership, • Enterprise LLM platforms: Vertex AI, Claude (Anthropic), or comparable, • Google Cloud Platform (GCP) or equivalent cloud infrastructure, • AI/ML evaluation and observability tooling , • Analytics and BI tooling: SQL, dashboarding, experimentation platforms, • Executive-grade presentation and reporting
Consultant – AI Product Transformation (Telecom) Full time
Remote USA
About the Role
We're hiring an AI Product transformation leader, to own strategy, roadmap, and business impact across a portfolio of AI-enabled products for an enterprise AI program. This is a multi-product leadership role: you own the roadmap and experimentation agenda for each product in the portfolio, and you're accountable for whether it drives measurable adoption and value, while reporting outcomes directly to senior executives and client stakeholders on that impact. This role will lead “executive level” program governance, stakeholder engagement, and ROI oversight for active engagement.
Responsibilities
Product & Program Ownership
- Own strategy and roadmap across the AI product portfolio, prioritized against market opportunity and current penetration by product, channel, and segment.
- Partner with engineering and AI/ML teams to translate strategy into shipped capability across products, while retaining accountability for outcomes.
Executive Communication & Governance
- Own the (bi)weekly/monthly executive governance cadence across the portfolio: prepare and defend performance, roadmap decisions, and investment tradeoffs under direct questioning.
- Build and maintain the standing reporting structure senior leadership uses to evaluate the program and for C-Level Steer-Co meetings.
Growth Strategy & Business Impact
- Drive the product-led growth motion for each product: activation, adoption, expansion.
- Design and run experiments, A/B tests, controlled rollouts, to establish causal impact rather than usage correlation; quantify results in business terms (time saved, revenue generated, conversion lift).
Go-to-Market Alignment & Adoption
- Align product priorities with GTM and business leadership to drive adoption in real-world workflows.
- Own the adoption and value-realization framework: track usage-to-value conversion and course-correct where adoption lags.
Strategic Advisory
- Act as strategic advisor to client and business stakeholders on how to apply the portfolio to their specific workflows and goals.
- Translate ambiguous business problems into structured, dated plans with clear tradeoffs.
Team Leadership & Stakeholder Partnership
- Direct team of Product Managers, UX/UI Leads and matrixed organization toward shared outcomes without directly owning technical delivery.
- Serve as the primary trusted point of contact for client and business stakeholders on program direction and results.
Qualifications
- 12-15 years in product management, including 3+ years at the Director (or above) level owning an AI-enabled or data-driven product portfolio with direct accountability for a growth or revenue metric.
- Track record presenting to, and being questioned by, senior or C-level leadership and client stakeholders.
- Background in management consulting or professional/IT services (strategy consulting, SI, or similar), with experience running executive governance processes.
- Demonstrated product-led growth experience: ownership of activation, adoption, or retention/expansion metrics.
- 3+ years of telecommunications industry experience
Tools & Skills
- Enterprise LLM platforms: Vertex AI, Claude (Anthropic), or comparable
- Google Cloud Platform (GCP) or equivalent cloud infrastructure
- AI/ML evaluation and observability tooling (evals, monitoring, drift and quality tracking)
- Analytics and BI tooling: SQL, dashboarding, experimentation platforms
- Executive-grade presentation and reporting: structured governance decks, board-level communication
Preferred Qualifications
- Masters, MBA or advanced degree
- Experience with telecom-specific technologies (e.g., OSS/BSS, 5G, AI/ML, cloud platforms)
- Familiarity with CRM, Sales Enablement and/or Marketing Enablement Systems
- Prior experience at a renowned or specialized consulting firm.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Bounteous, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Bounteous AI Hiring
Bounteous 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 $209,100 across 306 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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