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.

(ref:hirist.tech)

Artificial Intelligence Engineer