Job Description
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 LPA
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 Tensor Flow, Py Torch, 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.
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 LPA
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 Tensor Flow, Py Torch, 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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