Skill : GEN AI with GCP
Summary: We are seeking a seasoned Senior Data Scientist with overall 8 yrs and 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 design microservice-based solutions, containerize deployments (e.g., GKE), and drive end-to-end SDLC practices. Experience in the pharma domain is a strong advantage.
Required Qualifications
- Overall 8 yrs and 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.
Preferred Qualifications
- Experience in the pharmaceutical/pharma domain or regulated industries; familiarity with GxP, or similar data governance requirements.
- Exposure to other cloud providers (AWS/Azure) is a plus, but a strong preference for GCP.
- Experience with distributed training, large-scale data processing, and fine-tuning of large language models.
- Knowledge of privacy-preserving ML methods (differential privacy, synthetic data) and data lineage tools.
Education:
- Minimum qualification: Graduate degree in Information Technology.
- Preferred: Higher education (e.g., Master's degree in Computer Science, Information Technology, Data Science, or a related field) or relevant professional degrees/certifications.
