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Sr AI Engineer

Enable Data Incorporated

HybridHybrid

Original Job Summary

Sr AI Engineer

Primary Responsibilities:

  • Develop and improve AI agentic frameworks for complex tasks.
  • Develop novel data collection, fine-tuning, and AI technologies.
  • Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and evaluation.
  • Collaborate with cross-functional teams including AI researchers, ML engineers, and product teams.
  • Build scalable, reusable backend systems to support AI products.
  • Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance.
  • Utilize hands-on programming with modern languages such as Python and Scala.
  • Contribute to the architecture and design of large-scale distributed and ML systems.
  • Apply software engineering best practices including coding standards, code reviews, build processes, testing, and security.
  • Build AI Agents using Azure Copilot Studio or Mosaic AI Agent Framework.
  • Utilize strong experience with Azure and Databricks AI/ML services.
  • Leverage prior experience in developing AI/ML frameworks with Mosaic or open-source ML software.

Key skills/competency

AI frameworks, data collection, ML pipelines, Python, Scala, distributed systems, Azure, Databricks, AI Agents, software engineering

How to Get Hired at Enable Data Incorporated

🎯 Tips for Getting Hired

  • Research Enable Data Incorporated's culture: Study their mission, projects, and employee reviews.
  • Customize your resume: Highlight AI, ML, and software engineering skills.
  • Showcase relevant projects: Include distributed systems and pipeline experience.
  • Prepare technical examples: Demonstrate Azure, Databricks, and Python expertise.

📝 Interview Preparation Advice

Technical Preparation

Review ML pipeline design fundamentals.
Practice coding in Python and Scala.
Study Azure and Databricks AI services.
Understand distributed system architecture basics.

Behavioral Questions

Prepare examples of cross-team collaboration.
Demonstrate problem-solving in complex projects.
Discuss agile and review experiences.
Explain handling feedback and team challenges.