Technical Lead-App Development

Summary: We are seeking a seasoned 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 design microservice-based solutions, containerize deployments (eg, GKE), and drive end-to-end SDLC practices. Experience in the pharma domain is a strong advantage.
Key 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.

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 (eg, 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 communic