AI Developer

Job Title: AI Developer

Industry: Oil & Gas / Energy

Location: Dallas, Texas - onsite 5x a week

Assignment Type: Direct Hire

Pay: $115,000–$140,000 annually, plus a 15% short-term incentive and 20% long-term incentive.

Benefits: This position is eligible for medical, dental, vision, and 401(k). The company offers fully paid family benefits, a generous 401(k) match, and a competitive paid-time-off program.

Our client is an established energy organization investing in a new artificial intelligence and machine learning function. The company offers a collaborative, fast-moving environment where employees are empowered to make decisions, explore emerging technologies, and influence how AI is used across the business.

We are seeking an AI Developer to join a newly created AI/ML team and help transform emerging artificial intelligence capabilities into practical business solutions. This foundational team member will design, prototype, and deploy AI-enabled applications, intelligent agents, and automated workflows using Microsoft Azure, Azure Databricks, Azure AI Foundry, Snowflake, and modern large language model technologies.

This is a broad, hands-on role suited for an intellectually curious developer who enjoys experimenting with new tools and solving loosely defined problems. The ideal candidate combines software and data engineering fundamentals with genuine enthusiasm for generative AI. Because the field and team are still evolving, this person must be comfortable learning quickly, testing new approaches, and helping establish technical direction.

Key Responsibilities:

• Create and deploy AI-enabled applications using large language models and modern generative AI platforms.
• Develop autonomous and multi-agent solutions capable of using tools, retrieving information, and completing multistep tasks.
• Build agent-based workflows using technologies such as Azure AI Foundry Agent Service, Anthropic Claude, and comparable development frameworks.
• Connect Snowflake, Azure Databricks, and other enterprise data sources to AI applications and machine learning processes.
• Develop and maintain reliable data pipelines using SQL, Python, dbt, or similar technologies.
• Implement retrieval-augmented generation solutions that allow AI systems to securely use relevant organizational data.
• Create automated evaluation and testing methods to assess the accuracy, reliability, performance, and safety of models and agents.
• Quickly prototype emerging AI tools, frameworks, and models to determine whether they provide meaningful business value.
• Turn ambiguous business needs into functional prototypes and scalable production solutions.
• Partner with data scientists, data engineers, IT professionals, and business leaders throughout the development lifecycle.
• Help establish responsible AI practices related to privacy, governance, permissions, and secure data access.
• Document technical solutions and share findings, recommendations, and best practices with team members and stakeholders.
• Present technical recommendations confidently and respectfully challenge assumptions when another approach may produce a better result.

Qualifications:

• Bachelor’s degree in computer science, data science, engineering, mathematics, or a related discipline is required.
• Two to four years of experience developing and deploying software, data, AI, or machine learning applications in a production environment.
• Strong Python skills with experience producing organized, tested, and maintainable code.
• Working knowledge of SQL and experience with a cloud-based data platform such as Snowflake, Databricks, or a comparable technology.
• Practical exposure to developing applications with generative AI or large language model APIs, such as Claude, OpenAI, Azure OpenAI, or similar platforms.
• Understanding of software engineering practices, including Git or GitHub, automated testing, CI/CD, and common application design principles.
• Strong analytical and troubleshooting abilities with the capacity to work through unfamiliar or loosely defined problems.
• Effective communication skills and the ability to collaborate across engineering, data science, technology, and business groups.
• Demonstrated ability to independently learn new frameworks, platforms, and development approaches.
• Comfort working within a newly formed team where technologies, priorities, and processes may change quickly.