Junior Data Scientist and Gen Ai Architect
Position 1: Junior Data Scientist
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. Required
Qualifications:- 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).- 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. 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. Position 2: Gen AI Architect
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.- 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.- 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. Required
Qualifications:- Overall 10-12 years and minimum 5-7 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).- 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.
