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About This Role
Company Description:
The Company’s history goes back to December 18, 1934, with the creation of Hunt Oil Company by H. L. Hunt. Today, the Hunt Family of Companies has grown into a dynamic and diversified enterprise that operates across six areas of business: Hunt Oil Company, Hunt Refining Company, Hunt Energy Network, Hunt Realty Investments, Hunt Utility Services, and Hunt Innovative Technologies. Together, these businesses form Hunt Consolidated, Inc., a privately held, family owned enterprise dedicated to shaping the future.
Position Summary:
Our ideal candidate will serve as a Developer and Solution Architect for Hunt’s SAP Business Technology Platform (BTP) and BTP AI ecosystem. This role will build solutions spanning the SAP BTP CAP (Cloud Application Programming) Model, SAP Joule AI, Model Context Protocol (MCP) connectivity and AI development accelerators such as Anthropic Claude within BTP, while contributing to the solution designs behind them. They will work within the standards, patterns, reference architectures and guardrails that guide how BTP services, AI\-driven workflows and CAP model\-based applications are designed and delivered across BTP Solutions Delivery.
This role works with business and technology stakeholders to turn requirements into scalable, secure and well\-governed solutions that align with SAP best practices, architecture standards and Hunt’s digital transformation and AI strategy.
The Developer and Solution Architect will work with internal clients and cross\-functional teams to design, prototype, develop and deploy intelligent solutions leveraging the SAP BTP CAP model for rapid application development, SAP Joule AI copilot, MCP server connectivity patterns and AI\-powered development tools such as Claude for code generation, solution design acceleration and intelligent documentation. The role requires hands\-on skills in designing event\-driven architectures, API\-first strategies, CAP\-based application frameworks and AI\-augmented business processes. This role is expected to lean toward hands\-on development, with approximately 60% of time spent on development and 40% on architecture and solution design.
Responsibilities:
- Contribute to solution architecture for SAP BTP services (CAP model, Joule AI, SAP AI Core/AI Launchpad), participate in solution design and architecture reviews, and produce architectural blueprints, reference architectures, integration patterns, decision records and technical documentation aligned with Hunt's enterprise architecture roadmap
- Develop within the SAP BTP CAP model framework, building application patterns, component libraries and reusable templates that enable rapid, consistent CAP\-based application delivery across business units
- Use and promote AI development accelerators (Anthropic Claude, SAP Joule, GitHub Copilot) to accelerate solution design, code generation, test automation and technical documentation, and support development teams in adopting AI\-powered development workflows and best practices
- Design and develop MCP (Model Context Protocol) connectivity between SAP systems, AI agents and third\-party services, including reusable templates and best practices for MCP server deployment and tool orchestration
- Prototype and develop AI/ML solutions using SAP AI Core, SAP AI Launchpad, Generative AI Hub and third\-party AI services (Azure OpenAI, Anthropic Claude), and support the integration of SAP Joule's natural language and generative AI capabilities into enterprise business processes
- Meet with internal clients to determine business, functional and technical requirements; participate in design, configuration, testing and deployment of BTP\-based solutions; and deliver tangible artifacts including architecture decision records, solution design documents, proof\-of\-concept implementations and integration runbooks
- Apply governance frameworks, security standards and data privacy controls for AI\-enabled and CAP model solutions; follow standard operating procedures and maintain updated ticketing; keep pace with SAP's evolving BTP and AI roadmap and contribute to continuous improvement of the platform and developer enablement
- Perform other duties and fulfill additional responsibilities as assigned
Required Skills:
- Hands\-on experience with SAP BTP services (Cloud Foundry and/or Kyma), including designing and developing CAP model applications — application patterns, data model design and lifecycle management
- Proficiency in at least one of JavaScript/Node.js, Python, Java, or ABAP for BTP extension development and AI service integration
- Demonstrated use of AI development tools (Anthropic Claude, GitHub Copilot, SAP Joule) to accelerate code generation, solution design, documentation and testing, including embedding AI copilot functionality into SAP workflows
- Preferred experience with SAP AI Core, AI Launchpad, Generative AI Hub and/or third\-party AI platforms (Azure OpenAI, AWS Bedrock, Anthropic Claude); familiarity with MCP and AI agent\-to\-system integration concepts, with hands\-on MCP server development a plus
- Strong understanding of event\-driven architecture, API\-first design and enterprise integration standards (OData, REST, IDoc, BAPI, RFC), plus working knowledge of S/4HANA or other SAP line\-of\-business integration touchpoints
- Excellent verbal and written communication skills, with the ability to explain solution designs and AI architectures to both technical and business audiences
- SAP BTP certifications (e.g., SAP Certified Technology Associate – SAP BTP) preferred
Required Experience:
- Bachelor’s degree in MIS, Computer Science, Information Technology, or a related discipline
- Minimum of two (2\) or more years of experience in SAP BTP development, cloud application development, or solution architecture
- Demonstrated experience with the SAP BTP CAP model or comparable pro\-code application development frameworks preferred
- Experience leveraging AI tools (Claude, Copilot, Joule) in a professional development or architecture context preferred
Company Highlights:
- For 90 years, we have nurtured creative ideas and turned them into successful realities using three core strategic pillars – creativity, excellence, and people.
- 401K – 12% employer contribution with no vesting period (6% Match and 6% non\-matching contribution)
- Highly competitive compensation
- Hybrid work options available for most roles
- Five to eight weeks of PTO annually based on years of experience; eleven additional holidays per calendar year
- All Medical/Dental/Vision benefits start day one with the company; low employee premiums
- Education Assistance Program
- Free covered employee parking for Dallas HQ based employees
- Free specialty coffee bar in the Dallas HQ
- Onsite breakfast and lunch area in the Dallas HQ
- Commitment to the following ideals:
+ Work/Life Balance
+ Ongoing professional development opportunities
An exceptional employee experience
+
*Hunt is committed to a diverse and inclusive workplace. Hunt is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.*
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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 Hunt Energy, 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 $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.
Hunt Energy AI Hiring
Hunt Energy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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