Description :

We are looking for a skilled Sr AI Engineer to join our team and contribute to the development of cutting-edge AI models. You will be responsible for designing and fine-tuning AI solutions, including GANs, deep learning architectures, and fine-tuning off-the-shelf models to enhance our AI-driven products. The ideal candidate will have a strong foundation in machine learning, experience with AI research and model optimization, and the ability to translate complex concepts into production-ready solutions.

Key Responsibilities :

AI Model Development & Fine-Tuning :

- Develop, train, and optimize deep learning models, including GANs, transformers, and diffusion models.

- Implement state-of-the-art machine learning techniques to improve model accuracy and efficiency.

- Lead the design and implementation of proprietary models from scratch, moving beyond pre-packaged libraries to build custom architectures specifically tailored for healthcare datasets.

Full-Cycle Training & Optimization :

- Oversee the entire pipeline from raw data ingestion and feature engineering to large-scale distributed training and hyperparameter tuning.

SOTA Implementation :

- Translate academic research and "state-of-the-art" (SOTA) papers into production-ready code, implementing custom loss functions and optimization techniques to improve accuracy and computational efficiency.

Research & Innovation :

- Stay up to date with advancements in AI, ML, and deep learning, integrating new techniques to enhance model performance.

- Experiment with novel architectures and contribute to research-based AI solutions.

Data Engineering & Preprocessing :

- Build efficient data pipelines for structured and unstructured datasets.

- Implement data augmentation, synthetic data generation, and feature engineering to improve model performance.

Model Evaluation & Optimization :

- Conduct rigorous A/B testing, hyperparameter tuning, and model benchmarking.

- Optimize models for latency, scalability, and resource efficiency in production environments.

- Apply NLP techniques to improve natural language understanding, entity recognition, and sentiment analysis.

Production Deployment & Performance Monitoring :

- Deploy models into production environments (AWS, Azure, GCP, etc.), ensuring scalability and robustness.

- Collaborate with MLOps engineers to integrate models into the pipeline and monitor their performance in real-world scenarios.

Code Quality & Documentation :

- Write clean, modular, and well-documented Python code using industry best practices.

- Maintain detailed documentation of model architecture, training processes, and deployment strategies.

Required Skills & Qualifications :

Technical Skills :

- Strong programming skills in Python, with experience in TensorFlow, PyTorch, JAX, or similar frameworks.

- Hands-on experience in GANs, fine-tuning LLMs, transformers, and diffusion models.

- Experience with model optimization techniques, including quantization and distillation.

- Knowledge of data engineering techniques for AI model training.

- Understanding of MLOps best practices, including model deployment and monitoring.

- Experience with cloud platforms (AWS, GCP, Azure) for model training and deployment.

- Strong problem-solving abilities and ability to work on real-world AI challenges.

Preferred Skills :

- Experience in multimodal AI models (e.g., combining text, vision, and speech).

- Familiarity with synthetic data generation and augmentation techniques.

- Experience contributing to AI research or open-source AI projects.

If you're passionate about pushing the boundaries of AI and working on high-impact projects, we'd love to hear from you.