AI/ML Model Developer

7 days ago

Lahore, Punjab, Pakistan beBee Careers Full time
Job Title: Artificial Intelligence Developer

About the Role:
The position of AI/ML Engineer at Datum Labs involves developing, deploying, and optimizing cutting-edge AI models. As a key member of the team, you will work on deep learning, generative AI, and neural representation learning, contributing to real-world applications in AI-driven data intelligence.

Your Responsibilities:
  • Design, train, and fine-tune AI models, including LLMs, diffusion models, and neural networks.
  • Build and enhance data pipelines & ETL workflows in collaboration with data engineers.
  • Optimize deep learning models for scalability, efficiency, and performance.
  • Implement and manage vector databases (Weaviate, Pinecone) for AI applications.
  • Collaborate with cross-functional teams to align AI initiatives with business objectives.
  • Ensure model explainability, fairness, and bias mitigation for responsible AI practices.
  • Lead customer discussions, providing insights and actionable recommendations.
  • Deploy models in production with intuitive, user-friendly interfaces.
  • Implement MLOps practices for model monitoring, retraining, and lifecycle management.

Requirements:
  1. Strong proficiency in Python and relevant libraries (PyTorch, TensorFlow, Pandas, NumPy).
  2. Hands-on experience with LLMs, Generative AI, Hugging Face Transformers, Diffusers.
  3. Knowledge of prompt engineering, model optimization, and fine-tuning techniques.
  4. Experience with data analysis, unstructured datasets, and model visualization tools.
  5. Familiarity with APIs for model integration (e.g., FastAPI, Flask).
  6. Solid grasp of SQL for structured data analysis.
  7. Understanding of cloud-based ML tools (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).
  8. Proficiency in version control systems like Git.
  9. Strong problem-solving skills with a keen eye for detail.
  10. Excellent communication and presentation skills for technical and non-technical stakeholders.

Nice-to-Have:
  1. Experience with distributed computing, GPU acceleration, and parallel processing.
  2. Knowledge of statistical modeling, Bayesian inference, and neural network architectures.
  3. Familiarity with CI/CD pipelines for ML model deployment.
  4. Exposure to MLOps tools like MLflow or Kubeflow.

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