Title:  Technical Lead-App Development

Area(s) of responsibility

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 (e.g., 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 (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 communic