AI Architect

Forum Energy TechnologiesHouston, TX
2d

About The Position

We are seeking a technically deep and visionary AI/ML Engineer to lead the development and deployment of agentic AI solutions and drive enterprise-wide data standardization. This role is pivotal to our AI transformation journey, enabling autonomous AI agents that streamline operations, enhance decision-making, and unlock new efficiencies across the organization. The ideal candidate will possess a hybrid skill set spanning machine learning, data engineering, and software development, with a passion for building scalable AI systems and a strong foundation in data governance.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
  • 5+ years of experience in machine learning engineering, data science, or AI solution development.
  • Proven experience deploying ML models into production environments using cloud platforms (e.g., Azure, AWS, GCP).
  • Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
  • Strong understanding of data structures, algorithms, and software engineering principles.
  • Experience with data governance, data quality frameworks, and enterprise data architecture.
  • Familiarity with MLOps tools and practices (e.g., CI/CD for ML, model monitoring, versioning).
  • Excellent communication skills and ability to collaborate across technical and non-technical teams.

Nice To Haves

  • Experience building or integrating AI agents or autonomous systems.
  • Knowledge of enterprise systems (e.g., ERP, CRM) and their data structures.
  • Familiarity with data visualization tools (e.g., Power BI / SSRS) and SQL.
  • Experience with multi-agent systems or agent orchestration frameworks.

Responsibilities

  • Design, develop, and deploy agentic AI systems that autonomously execute multi-step workflows across business functions (e.g., IT, HR, Finance, Operations).
  • Collaborate with cross-functional teams to identify high-impact AI use cases and translate them into technical solutions.
  • Build and maintain robust ML pipelines, including data ingestion, model training, deployment, and monitoring (MLOps).
  • Lead data standardization initiatives to ensure high-quality, consistent, and AI-ready data across systems.
  • Partner with data engineering and IT teams to define and implement data governance frameworks, taxonomies, and metadata standards.
  • Monitor and optimize AI model performance in production environments, ensuring reliability, scalability, and alignment with business goals.
  • Serve as a technical advisor on AI/ML best practices, tools, and emerging technologies.
  • Support change management efforts to improve data literacy and promote standardization best practices
  • Stay ahead of industry trends and maintain compliance with evolving regulations affecting AI and data.
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