Seeking a Senior GenAI Developer with extensive experience in building and deploying sophisticated GenAI and machine learning models. This role focuses on designing and implementing microservice-based solutions within cloud environments, particularly Google Cloud Platform (GCP). Your expertise will be crucial in driving end-to-end SDLC practices and ensuring the successful deployment and management of AI models.

Experience within the pharmaceutical sector is highly valued and will be a significant advantage. You will be instrumental in shaping the future of our AI initiatives and contributing to innovative solutions.

Lead the entire lifecycle of GenAI/ML model development, from problem definition and data preparation to model training, evaluation, and iteration. Architect and implement robust AI solutions using microservices, deploying them in containerized environments like GKE, and defining clear APIs and data contracts. Utilize a comprehensive suite of GCP services, including Vertex AI, BigQuery, and Dataflow, to engineer scalable AI applications and optimize data workflows. Demonstrate a strong understanding of Retrieval-Augmented Generation (RAG) principles and processes. Manage the deployment, monitoring, and maintenance of models in production, focusing on observability, cost efficiency, and performance enhancement. Collaborate effectively with cross-functional teams, translating complex business requirements into actionable and resilient ML solutions. Champion adherence to SDLC standards throughout the development process, promoting best practices and reusable patterns. Mentor and guide junior team members, contributing to a culture of continuous learning and knowledge sharing. Stay abreast of the latest GenAI advancements, actively evaluating new technologies and approaches to foster reproducible research and development.

Possess 10-12 years of overall professional experience, with a minimum of 5-7 years dedicated to hands-on development and deployment of GenAI/ML models in cloud settings. Demonstrate advanced proficiency with Google Cloud Platform (GCP) and its suite of AI/ML services, such as Vertex AI, BigQuery, and GKE. Must have practical experience utilizing agentic frameworks. Exhibit a solid grasp of Retrieval-Augmented Generation (RAG) concepts and their application. Showcase strong software engineering capabilities, including expertise in Python, familiarity with ML frameworks like TensorFlow, PyTorch, or scikit-learn, and experience in developing REST or GraphQL APIs. Proven experience in designing and deploying microservices architectures and containerized solutions using Docker and Kubernetes, with a preference for GKE. Possess substantial MLOps experience, covering model versioning, experiment tracking, automated training pipelines, feature stores, model registries, monitoring, and governance. Exhibit strong data processing and analytics skills, including SQL, data pipeline development, ETL/ELT concepts, data quality management, and supporting data visualization efforts. Possess excellent problem-solving, communication, and collaboration skills, enabling effective work within diverse, interdisciplinary teams. Understand fundamental cloud security principles, IAM, and data privacy/compliance standards. Demonstrate a clear ability to translate business challenges into scalable ML solutions and articulate technical details to non-technical audiences.