We are looking for a Junior Data Scientist with at least 5 years of hands-on experience in developing GenAI/machine learning models and deploying them in a cloud environment, preferably on Google Cloud Platform (GCP). The ideal candidate will develop microservice-based solutions, containerize deployments (e.g., GKE), and drive end-to-end SDLC practices. Experience in the pharma domain is a strong advantage.

Responsibilities

Work on end-to-end development of GenAI/ML models: problem framing, data preparation, model selection, training, evaluation, and iteration.
Implement microservice-based AI solutions and deploy them in containerized environments (preferably GKE); define APIs and data contracts.
Leverage GCP offerings (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE, etc.) to develop scalable AI solutions and efficient data workflows.
Deploy, monitor, and maintain models in production; implement observability (logs, metrics, tracing), cost optimization, and performance tuning.
Ensure cloud security, data governance, and compliance in line with regulatory requirements; manage IAM roles, data access controls, and data lineage.
Collaborate with cross-functional teams (data engineers, software engineers, product, regulatory/compliance, analytics) to translate business needs into robust ML solutions.
Stay current with GenAI advancements and evaluate new tools/approaches; produce reproducible experiments and artifacts.

Requirements

At least 5 years of hands-on experience in developing GenAI/machine learning models.
Experience deploying models in a cloud environment, preferably Google Cloud Platform (GCP).
Proficiency in developing microservice-based solutions.
Experience with containerized deployments (e.g., GKE).
Strong understanding of end-to-end SDLC practices.