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About This Role
Oxy produces , markets and transports oil and natural gas to maximize value and provide resources fundamental to life. The company leverages its global leadership in carbon management to advance lower\-carbon technologies and products. Headquartered in Houston, Oxy primarily operates in the United States, Middle East and North Africa. To learn more, visit Oxy
Oxy strives to attract and retain talented employees by investing in their professional development and providing rewarding opportunities for personal growth. Our goal is to meet the highest employer standards by ensuring the health and safety of our employees, protecting the environment and positively impacting our communities where we do business.
Advisor – Data Trust \& AI Products
Job Summary
Are you passionate about building AI\-powered products that transform how people work while ensuring the data behind them is trusted, protected, and business\-ready?
Oxy is seeking a unique technical leader who thrives at the intersection of Data Quality, Data Governance, AI Development, and Product Innovation. This highly visible role will spend approximately 50% of its time driving enterprise data quality and data trust initiatives and 50% building AI\-powered products, copilots, intelligent agents, and data\-driven solutions that create measurable business value.
You will play a critical role in shaping Oxy's AI future by ensuring our data is trusted, protected, and AI\-ready while simultaneously developing innovative products that leverage that foundation. This role offers a rare opportunity to influence enterprise data strategy, build cutting\-edge AI solutions, and drive transformation across the organization.
What You'll work on
Data Trust \& Enterprise Data Quality
- Establish and lead enterprise data quality measurement across structured data domains.
- Define data quality KPIs, scorecards, and executive dashboards.
- Implement automated monitoring and controls for completeness, accuracy, consistency, and timeliness.
- Drive enterprise data classification, protection, and governance initiatives.
- Partner with business leaders and data owners to improve data quality and accountability.
- Enable trusted, governed, and AI\-ready data assets across the enterprise.
- Support metadata, lineage, stewardship, and data catalog initiatives.
AI Product Development \& Innovation
- Design, build, and deploy AI\-powered applications, copilots, intelligent agents, and business solutions.
- Partner directly with business teams to identify opportunities where AI can drive measurable value.
- Develop GenAI solutions leveraging LLMs, RAG architectures, enterprise knowledge bases, and agentic workflows.
- Build prototypes, MVPs, and production\-ready AI products that solve real business problems.
- Integrate enterprise data sources into AI applications while ensuring governance and security requirements are met.
- Evaluate emerging AI technologies and drive innovation across the enterprise.
- Translate business challenges into scalable AI products and solutions.
Required Qualifications
- 10\+ years of experience in Data Management, Data Engineering, Data Governance, AI Development, or Product Engineering.
- Strong expertise in SQL, Python, and cloud\-native development.
- Hands\-on experience with Databricks \& AWS.
- Experience developing AI solutions using LLMs, RAG, vector databases, AI agents, or enterprise AI platforms.
- Strong understanding of enterprise data quality, governance, metadata, and data protection.
- Experience delivering products from concept through deployment and adoption.
- Ability to work directly with business stakeholders to identify opportunities and rapidly build solutions.
- Experience with CI/CD pipelines, Azure DevOps, GitHub, or equivalent development platforms.
- Experience deploying AI applications in cloud environments including AWS and Azure.
- Understanding of MLOps practices including model lifecycle management, monitoring, observability, and performance optimization.
- Experience implementing logging, monitoring, telemetry, and usage analytics for AI applications.
- Knowledge of security, authentication, authorization, and enterprise architecture principles.
Preferred Experience
- Experience building enterprise copilots, knowledge assistants, or conversational AI solutions.
- Experience with Microsoft Copilot Studio, Power Platform, and enterprise automation technologies.
- Experience building AI solutions using enterprise data platforms such as Databricks.
- Experience leveraging enterprise data catalogs, metadata, lineage, and governance capabilities within AI solutions.
- Experience working with structured and unstructured data at enterprise scale.
Recruitment Fraud
It has come to our attention various individuals and/or organizations are contacting people falsely pretending to recruit on behalf of Oxy. Please be aware that these recruiting scams and communications do not originate nor are they associated with our recruitment process. All Oxy job postings and offers will require a completed application through our company website.
Oxy does not charge a fee at any stage of the recruiting process. We will never:
- Ask you to pay for applications, interviews, meetings, processing, training or for any other fees
- Use recruiting or placement agencies that charge candidates an advance fee of any kind or
- Request personal information such as passport and bank account details at an early stage of our recruitment process.
We recommend against responding to unsolicited business propositions or offers from people you don't know. Do not disclose your personal or financial details. If you believe you have been the victim of a recruiting scam, please contact your local police department.
All qualified applicants will receive consideration for employment without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.
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 Occidental, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
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.
Occidental AI Hiring
Occidental has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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