Gen AI Architect
Summary: We are seeking seasoned Senior developer GenAI with overall 10-12 yrsand at least 5-7 years of hands-on experience in developing GenAI/machine learningmodels and deploying them in a cloud environment, preferably on Google CloudPlatform (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 ResponsibilitiesLead 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 incontainerized environments (preferably GKE); define APIs and data contracts.Leverage GCP offerings (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, CloudRun, GKE, etc.) to design scalable AI solutions and efficient data workflows.Knowledge of Retrieval-Augmented Generation (RAG) concepts and processesProficiency with Google Cloud Platform (GCP) and its AI/ML offerings (e.g., Vertex AI,BigQuery, Dataflow, Cloud Storage, GKE).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 bestpractices.Mentor and guide junior scientists; contribute to code reviews, standards, andknowledge sharing.Stay current with GenAI advancements and evaluate new tools/approaches; producereproducible experiments and artifacts.
Required QualificationsOverall 10-12yrs and Minimum 5-7 years of hands-on experience developing GenAI/MLmodels 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 frameworkKnowledge of Retrieval-Augmented Generation (RAG) concepts and processesStrong 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 containerizedsolutions (Docker, Kubernetes; preference for GKE).Solid experience in MLOps: model versioning, experiments, automated training, featurestores, model registries, monitoring, and governance.Data processing and analytics expertise: SQL, data pipelines, ETL/ELT concepts, dataquality, and data visualization support.Excellent problem-solving, communication, and collaboration skills; ability to work withcross-disciplinary teams.Understanding of cloud security concepts, IAM, and basic principles of data privacy andcompliance.Demonstrated ability to translate business problems into scalable ML solutions and tocommunicate technical concepts to non-technical stakeholders.Preferred QualificationsExperience in the pharmaceutical/pharma domain or regulated industries; familiaritywith GxP, or similar data governance requirements.Exposure to other cloud providers (AWS/Azure) is a plus, but a strong preference forGCP.Experience with distributed training, large-scale data processing, and fine-tuning oflarge language models.Knowledge of privacy-preserving ML methods (differential privacy, synthetic data) anddata 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 professionaldegrees/certifications.
