Artificial Intelligence Engineer

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.Compensation: 15 - 20 LPAKey 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.

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