About the role

Seasoned Data Scientist developing and deploying GenAI/machine learning models on GCP. Leading microservice-based solutions and MLOps practices with a focus on the pharma domain.

Responsibilities

Lead end-to-end development of GenAI/ML models: problem framing, data preparation, model selection, training, evaluation, and iteration.
Architect and implement microservice-based AI solutions and deploy them in containerized environments (preferably GKE); define APIs and data contracts.
Incorporate and operationalize defined ML pipelines with MLOps practices: model versioning, feature stores, experiment tracking, CI/CD for ML, monitoring, and rollback strategies.
Leverage GCP offerings (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE, etc.) to design 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.
Uphold SDLC standards: requirements gathering, design, development, testing, deployment, maintenance, and documentation; promote reusable patterns and best practices.
Mentor and guide junior scientists; contribute to code reviews, standards, and knowledge sharing.
Stay current with GenAI advancements and evaluate new tools/approaches; produce reproducible experiments and artifacts.

Minimum 5 years of hands-on experience developing GenAI/ML models and deploying them in a cloud environment.
Proficiency with Google Cloud Platform (GCP) and its AI/ML offerings (e.g., Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run, GKE).
Must have experience working with any agentic framework
Knowledge of Retrieval-Augmented Generation (RAG) concepts and processes
Strong software engineering skills: Python (primary), experience with ML frameworks (TensorFlow, PyTorch, scikit-learn), and API development (REST/GraphQL).
Experience designing and deploying microservices architectures and containerized solutions (Docker, Kubernetes; preference for GKE).
Solid experience in MLOps: model versioning, experiments, automated training, feature stores, model registries, monitoring, and governance.
Data processing and analytics expertise: SQL, data pipelines, ETL/ELT concepts, data quality, and data visualization support.
Excellent problem-solving, communication, and collaboration skills; ability to work with cross-disciplinary teams.
Understanding of cloud security concepts, IAM, and basic principles of data privacy and compliance.
Demonstrated ability to translate business problems into scalable ML solutions and to communicate technical concepts to non-technical stakeholders.