Key Responsibilities :

- Design and implement end-to-end machine learning pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment, to deliver scalable and reliable AI solutions.

- Develop and fine-tune Generative AI models and Large Language Models (LLMs) using Python and relevant frameworks, to address specific client needs and improve model performance.

- Build and maintain robust data pipelines using technologies like Spark, Kafka, or similar, to ensure efficient and reliable data flow for AI model training and inference.

- Collaborate with cross-functional teams to integrate AI models into existing systems and applications, to enhance functionality and improve user experience.

- Implement MLOps best practices, including model monitoring, versioning, and automated deployment, to ensure the reliability and maintainability of AI solutions.

- Research and evaluate new AI technologies and techniques, to identify opportunities for innovation and improvement in our AI solutions.

Required Skillset :

- Demonstrated ability to design, develop, and deploy machine learning models using Python and relevant libraries such as TensorFlow, PyTorch, or scikit-learn.

- Proven experience in working with Generative AI models and Large Language Models (LLMs).

- Strong understanding of deep learning concepts and neural network architectures.

- Experience in building and maintaining data pipelines using technologies like Spark, Kafka, or similar.

- Proficiency in Java for building scalable and robust applications.

- Familiarity with MLOps principles and tools for model monitoring, versioning, and deployment.

- Excellent communication and collaboration skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences.

- Bachelor's or Master's degree in Computer Science, Data Science, or a related field.