Interested in this AI/ML Engineer role at CloudFactory?
Apply Now →About This Role
At CloudFactory, we are a mission-driven team passionate about unlocking the potential of AI to transform the world. By combining advanced technology with a global network of talented people, we make unusable data usable, driving real-world impact at scale.
More than just a workplace, we’re a global community founded on strong relationships and the belief that meaningful work transforms lives. Our commitment to earning, learning, and serving fuels everything we do as we strive to connect one million people to meaningful work and build leaders worth following.
Our Culture
At CloudFactory, we believe in building a workplace where everyone feels empowered, valued, and inspired to bring their authentic selves to work. We are:
- Mission-Driven: We focus on creating economic and social impact.
- People-Centric: We care deeply about our team’s growth, well-being, and sense of belonging.
- Innovative: We embrace change and find better ways to do things together.
- Globally Connected: We foster collaboration between diverse cultures and perspectives.
If you’re passionate about innovation, collaboration, and making a real impact, we’d love to have you on board!
About the Role
Energy, Oil & Gas is one of the most operationally complex and asset-intensive industries, where AI is rapidly moving from experimentation to enterprise-scale deployment.
While the opportunity is significant, many organizations struggle to move from promising pilots to production-grade AI due to fragmented data, complex environments, and strict safety and regulatory requirements.
CloudFactory is built to solve exactly this.
As Client Principal, AI Consulting (Energy, Oil & Gas), you will be the senior commercial and strategic leader for our energy sector. You will operate as a trusted advisor to executive stakeholders across operators, oilfield services companies, and energy enterprises, shaping AI transformation agendas and leading engagements from origination through delivery oversight.
This is a high-impact, externally facing role for someone who can open doors, lead complex conversations, and convert opportunity into long-term partnerships.
What You’ll Do
Executive Relationships & Strategic Advisory
- Build and lead market growth strategy across energy operators, oilfield services companies, and energy enterprises.
- Operate as a strategic advisor on AI adoption, from identifying high-value use cases to scaling solutions in production.
- Engage C-suite and VP stakeholders as a trusted advisor, not a vendor.
- Navigate complex stakeholder environments across operations, subsurface, engineering, HSE, IT, and data teams.
- Develop a deep understanding of client priorities across upstream, midstream, and downstream operations.
Business Development & Growth
- Own new client acquisition, account expansion and P/L management accounts within the energy sector.
- Develop and execute strategic account plans with clear pathways from pilot to enterprise-scale programs.
- Lead consultative, executive-level sales conversations and build compelling business cases for AI investment.
- Own the full commercial lifecycle: discovery, solution framing, proposal development, and executive presentations.
- Navigate sector-specific buying cycles, procurement processes, and capital planning timelines.
Solution Shaping & Delivery Oversight
- Identify and shape high-impact AI use cases, including:
- + Predictive maintenance and asset monitoring + Computer vision for inspection (drone, aerial, and field imagery) + Safety and compliance automation + Intelligent document processing from engineering documentation + Geospatial, LiDAR, and digital twin data pipelines
- Translate operational challenges into scalable AI solutions leveraging CloudFactory’s capabilities.
- Partner closely with technical and delivery teams to ensure high-quality execution and client satisfaction.
- Oversee delivery to ensure alignment on scope, timelines, and outcomes.
Thought Leadership & Industry Impact
- Stay at the forefront of AI trends across the energy value chain, including asset digitization, predictive operations, and computer vision at scale.
- Contribute to CloudFactory’s external presence through industry events, client engagements, and thought leadership.
- Bring sector insights into product, delivery, and go-to-market strategy.
Leadership & Collaboration
- Lead and inspire cross-functional teams including consultants, data scientists, and engineers.
- Foster strong collaboration between client stakeholders and internal teams.
- Promote best practices in AI consulting, delivery excellence, and client engagement.
Requirements
- Deep industry expertise in energy, oil & gas, or adjacent industrial sectors, with a strong understanding of operations and digital transformation.
- Established executive network across energy operators or oilfield services companies that you can activate from day one.
- Strong business acumen, with the ability to translate operational challenges into AI-driven opportunities; P/L management experience.
- Technical fluency in AI/ML concepts, including predictive analytics and computer vision.
- Proven consulting and commercial experience, managing enterprise clients and complex engagements.
- Executive presence and communication skills, with the ability to engage senior stakeholders as a trusted peer.
- Leadership experience, guiding cross-functional teams and delivering high-impact outcomes.
- Experience: 10+ years of experience in consulting, technology, or digital transformation within energy or industrial sectors.
- Education: Bachelor’s degree in computer science, engineering, statistics, or related field. Advanced degrees in AI, data science, or business are a plus.
Why This Role
This is an opportunity to operate at the intersection of AI, industry transformation, and executive leadership, helping some of the world’s largest energy companies unlock real value from AI. You’ll play a critical role in shaping both client outcomes and CloudFactory’s growth in a strategically important sector.
Benefits* Compensation: Highly competitive base salary and an aggressive, performance-aligned incentive compensation structure.
- Comprehensive Benefits: Robust health, dental, vision, and retirement plans.
- Cutting-Edge Projects: Direct exposure to, and leadership of, advanced AI initiatives with the world's most influential energy corporations.
- Collaborative Culture: A supportive, high-performance team environment focused on innovation and measurable client success.
- Professional Growth: Structured access to continuous executive education, mentorship, and accelerated leadership development opportunities.
- Executive Visibility: Regular interaction with, and direct accountability to, the company’s senior leadership team.
This role represents a unique, high-impact opportunity to define and shape the AI landscape for major global corporations in the energy sector. If you are a highly consultative professional with deep sector knowledge, a passion for AI, and a proven talent for originating and closing complex, strategic enterprise deals, we encourage your application.
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 CloudFactory, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
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.
CloudFactory AI Hiring
CloudFactory 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 $207,800 across 271 tracked positions. That's 3% below the national 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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