
AI/ML Engineer
3 weeks ago
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.
Communication, Leaves, Medical, Provident Fund
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