AI/ML Engineer 2

Datum Labs is seeking a passionate and results-driven AI/ML Engineer to develop, deploy, and optimize cutting-edge AI models.

In this role, you will work on deep learning, generative AI, and neural representation learning, contributing to real-world applications in AI-driven data intelligence.

You will collaborate with cross-functional teams, participate in customer-facing discussions, and ensure our models deliver actionable insights that drive business value.

Key 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

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

Nice-to-Have

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