AI Scientist

Job Description

Job Description

Role Description :

o Design and develop ML systems : Choose the right algorithms, designing data pipelines, and building scalable models.

o Data preparation and feature engineering : Gather, clean, and transform data into a usable format for training models.

o Model training and evaluation : Train models on the prepared data, evaluating their performance using metrics, and fine-tuning them to achieve optimal results.

o Model deployment and monitoring : Deploy models into production on-prem or on cloud infrastructure, ensuring their reliability and performance, and monitoring their performance over time.

o Collaboration : Work with data scientists, software engineers, and domain / business experts to develop and implement ML solutions.

Essential Skills :

o Strong experience with Deep Learning and NLP

o Hands on working knowledge of interfacing with LLM APIs and LLM agents, prompts and responses

o Strong programming skills in languages such as Python, java, or C++. Experience with cloud platforms (AWS, GCP, and Azure) and MLOps tools.

o 3+ years of hands-on experience in machine learning development, and experience leading a team of machine learning developers or engineers.

o Strong understanding of software development principles, including design patterns, testing, and deployment.

o Experience with DevOps practices such as CI / CD, experience with containerization using Docker and Kubernetes.

o Strong understanding of application implementation requirements, including risk, privacy, and compliance.

o Excellent communication and leadership skills, with the ability to work effectively with cross-functional teams

o Previous experience with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, Flyte, or MetaFlow;o Familiarity with machine learning frameworks such as PyTorch, TensorFlow and / or similar

Desirable Skills :

o Familiarity with Snowflake, Airflow, or similar orchestration and warehousing platforms

o Understanding of CI / CD principles, version control, and production deployment best practices