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About This Role
Overview:
Diversified Gas \& Oil Corporation (“DGOC”) is a wholly\-owned subsidiary of Diversified Energy Company PLC, a US\-based company listed on the New York Stock Exchange (NYSE) and London Stock Exchange (LSE) under the ticker symbol “DEC”. Diversified Gas \& Oil Corporation (DGOC) is an established owner and operator of producing conventional and unconventional natural gas \& oil wells and midstream pipelines and compression stations concentrated in the Appalachian Basin in the United States. Headquartered in Birmingham, AL, our field operations are located throughout the Appalachian Basin in the states of Tennessee, Kentucky, Virginia, West Virginia, Ohio, and Pennsylvania. In 2021, Diversified announced our expansion into our Central Regional Focus Area, which includes producing areas within Louisiana, Texas, Oklahoma, and Arkansas.
Responsibilities:
POSITION SUMMARY \& RESPONSIBILITIES:
The Director of AI serves as the hands\-on technical leader within our AI Enablement team, responsible for building, supporting, and hardening the AI solutions and platforms that power our enterprise portfolio. Reporting to the VP of AI Enablement, this is first and foremost a builder’s role—spending the majority of the time designing and supporting AI solutions, resolving complex technical challenges, and evaluating emerging tools, while also coordinating delivery, managing partners, and enabling users. Working closely with the VP of AI—who leads strategy and executive engagement—this role turns strategy into reliable, working solutions.
Responsibilities:
- Design, build, and support generative and agentic AI solutions using Microsoft Copilot, Copilot Studio, Azure AI Foundry, and the Power Platform.
- Act as the technical problem\-solver—diagnosing and resolving agent failures, workflow breakdowns, integration errors, and performance or accuracy issues.
- Evaluate, pilot, and benchmark emerging AI tools and models, and recommend fit\-for\-purpose approaches.
- Use Python and scripting for data preparation, automation, and rapid proof\-of\-concept development.
- Establish technical standards for prompt engineering, agent configuration, and reusable frameworks.
- Manage the promotion of solutions through Development, UAT, and Production environments, following DevOps and release\-management best practices.
- Translate business needs into technical solution approaches, integration points, and data dependencies, partnering with Data, IT, Infrastructure, and Cybersecurity teams on architecture, data sourcing, and deployment.
- Support the transition of successful prototypes into scalable, governed, production\-ready solutions and drive adoption across the organization.
- Identify, engage, and get maximum value from external experts, vendors, and delivery partners—leveraging specialized expertise rather than building everything in\-house.
- Align partner workstreams to the roadmap, ensure adherence to architecture, security, and governance standards, and manage cost\-to\-value outcomes across vendor engagements.
- Help keep active AI initiatives on track alongside our PMO/BPA functions, and surface risks and resourcing needs early to the VP of AI Enablement.
- Provide training and hands\-on support on the AI tools and solutions this role builds, and create practical documentation and standard operating procedures (SOPs) so solutions can be used and maintained reliably.
- Support business champions (e.g., the AI SWAT Team) and help build AI fluency across the organization.
Qualifications:
POSITION REQUIREMENTS:* 5\+ years of experience in software development, automation, IT solutions, analytics, or a related technical field.
- Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related technical field strongly preferred, or an equivalent combination of education and experience.
- Hands\-on experience with the Microsoft ecosystem—Azure, Microsoft 365, and the Power Platform.
- Practical experience building automations and workflows with Power Automate (cloud and/or desktop flows).
- Experience deploying, integrating, or managing solutions in Azure (e.g., functions, storage, identity, or AI services).
- DevOps experience promoting products through Development, UAT, and Production environments, including release management and deployment pipelines.
- Working knowledge of Python for automation, data processing, and prototyping.
- Hands\-on experience with Copilot Studio, Azure AI Foundry, or building AI agents, with familiarity in large language models, prompt engineering, and agent orchestration.
- Strong troubleshooting and debugging skills across applications, workflows, integrations, and data pipelines.
- Experience managing vendors or delivery partners, including balancing cost against value delivered.
- Ability to explain technical concepts clearly through training, documentation, and hands\-on support, and to coordinate multiple projects and partners while keeping delivery on track.
- Strong communication skills and comfort working with both technical teams and business stakeholders.
Requested Skills, Knowledge, and Abilities:
Microsoft Copilot, Copilot Studio, Azure AI Foundry, Microsoft Power Platform, Power Automate, Microsoft Azure, Microsoft 365, Python, Large Language Models, Prompt Engineering, Agent Orchestration, DevOps / CI\-CD, Dev\-UAT\-Prod Release Management, Snowflake, Model Context Protocol (MCP), Unstructured\-to\-Structured Data (OCR / Document Intelligence), AI Governance, Problem Solving, Troubleshooting, Vendor \& Partner Management, Cost\-to\-Value Analysis, Consultative / Advisory Experience, Training \& Enablement, Technical Documentation, Multi\-tasking, Coordination, Technical Zeal.
Office Physical Requirements and Working Conditions:* Prolonged periods working at a desk in front of a computer.
*Additional Requirements:** Must be able to lift up to 15 pounds at a time.
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 Diversified Gas & Oil, 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. Director-level AI roles across all categories have a median of $272,150.
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.
Diversified Gas & Oil AI Hiring
Diversified Gas & Oil has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Birmingham, AL, 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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