Software Engineer - Enterprise AI
- Architect AI-Enabled Solutions:
Design and implement end-to-end software solutions that integrate AI/ML models and services to improve decision-making, workflow automation, and operational efficiency. - Develop and Deploy Applications:
Build, test, and operationalize cloud-native applications using Microsoft Azure (App Services, Functions, AKS) and Databricks, while maintaining flexibility to leverage other AI services as business needs evolve. Ensure solutions are scalable, secure, and future-proofed. - Enable Reusable Software Assets:
Create modular, reusable libraries, APIs, and templates that support multiple AI applications and accelerate delivery across the enterprise. - Collaborate Across Disciplines:
Work as part of AI delivery teams, including data scientists, AI engineers, and applied scientists to integrate AI capabilities into production systems. - Cloud-Native Engineering & DevOps:
Implement Chevron standards and best practices for CI/CD, infrastructure-as-code, and automated testing using Azure DevOps, Git, and containerization tools. - Quality, Security & Observability:
Apply secure coding practices, automated testing, and monitoring to ensure reliability and compliance. - Innovation and Continuous Learning:
Stay ahead of emerging trends in agentic AI, generative AI, and modern software engineering, applying cutting-edge techniques to Chevron's most critical business challenges.
- Bachelor's degree in computer science, Engineering, or a related field (or equivalent experience).
- 5+ years of prior experience as a software engineer building cloud-native applications in enterprise environments.
- Strong proficiency in one or more programming languages: Python, Jav, C#/.NET, or TypeScript/Node.js.
- Skilled in designing and implementing cloud-native solutions using a wide stack of Microsoft Azure services, including Azure App Services, Azure. Functions, Azure DevOps, Azure Data Factory, Azure SQL Database, among others.
- Solid understanding of software design patterns, API development, event-driven architectures, and secure coding practices.
- Familiarity with DevOps, CI/CD pipelines, automated testing frameworks, and container orchestration (e.g., Docker, Kubernetes).
- Experience with integrating AI/ML models into production applications.
- Familiarity with agentic AI architectures, LLM orchestration, and vector search.
- Knowledge of AzureML, Databricks Genie, and other AI platforms.
- Experience with observability frameworks, performance optimization, and cost-efficient cloud design.
- Relevant Microsoft Azure certifications.
- Skilled in ability to design and implement security best practices and compliance requirements in the oil and gas industry.
- Excellent problem-solving skills and attention to detail. Strong communication and collaboration skills.
- Strong technical leadership and mentoring capabilities.
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