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AI Model Developer

1 week ago

Taguig, National Capital Region, Philippines DXC Technology Philippines Full time

Overview

DXC Technology Philippines is seeking a skilled AI Engineer to join our team in Taguig. As an AI Engineer, you will be responsible for designing, developing, deploying and maintaining AI models and systems.

About the Job

This is a hybrid position that requires 3-5 years of experience. The ideal candidate should have a strong background in machine learning, software development, and a passion for innovation.

Key Responsibilities:

  1. Develop, train, and validate machine learning models.
  2. Experiment with different algorithms and architectures to solve complex problems.
  3. Fine-tune models for optimal performance.
  4. Apply techniques such as regularization, cross-validation, and hyperparameter tuning.
  5. Data Preparation and Pre-processing:
    1. Collect, clean, and preprocess raw data.
    2. Conduct exploratory data analysis.
    3. Transform data into a format suitable for training AI models.
  6. Deploy trained models into production environments.
  7. Integrate models into existing systems.
  8. Implement APIs for model inference.
  9. Monitor and maintain deployed models.
  10. Performance Monitoring and Optimization:
    1. Monitor the performance of deployed models.
    2. Identify and fix issues related to model performance.
    3. Optimize models for speed, accuracy, and resource utilization.
    4. Implement A/B testing to evaluate model changes.
  11. Work closely with cross-functional teams, including software engineers, data scientists, and domain experts.
  12. Communicate effectively with non-technical stakeholders.
  13. Participate in code reviews and design discussions.
  14. Develop and optimize Langchain-heavy applications for various LLM-driven workflows.
  15. Design, build and maintain API endpoints written in Flask to expose LLM models.
  16. Integrate LLMs (Huggingface, OpenAI, etc.) into scalable applications.
  17. Work with vector databases (ChromaDB, Redis) for RAG process.
  18. Implement prompt engineering strategies to improve model performance.
  19. Deploy and manage models/projects on AWS (EC2) and/or Openshift using Docker containers.