Data Science & GenAI Specialist

As a Data Science & GenAI Specialist (AI/ML Engineer) GCP, your role involves leveraging your expertise in Machine Learning, MLOps/AIOps, Generative AI, and Google Cloud Platform (GCP) to design, develop, and deploy enterprise-scale AI solutions. You will be responsible for implementing various AI solutions, including LLM-powered applications, AI Agents, RAG architectures, and machine learning models in production environments. Key Responsibilities: - Generative AI & LLM Solutions - Design, develop, and deploy enterprise-grade Generative AI applications using Google Gemini models. - Build and optimize Retrieval-Augmented Generation (RAG) architectures using enterprise knowledge sources. - Develop AI-powered assistants, copilots, and intelligent automation solutions. - Implement prompt engineering, prompt optimization, and evaluation frameworks. - Fine-tune and customize foundation models where required. - AI Agent Development - Design and implement AI Agents using Agent Development Kit (ADK) and Agent-to-Agent Protocol (A2A). - Deploy and manage AI Agents on GCP infrastructure. - Build multi-agent orchestration frameworks for enterprise use cases. - Data Science & Machine Learning - Perform exploratory data analysis (EDA), feature engineering, model training, validation, and deployment. - Build predictive and prescriptive analytics solutions. - Develop and maintain ML pipelines supporting business-critical applications. - Apply advanced statistical and machine learning techniques to solve complex business problems. - MLOps & AIOps - Build scalable MLOps pipelines for model training, deployment, monitoring, and governance. - Implement model observability, drift detection, and performance monitoring. - Automate AI/ML lifecycle management through CI/CD pipelines. - Drive AIOps initiatives to improve operational efficiency and reliability. - Cloud & Production Deployment - Design and deploy scalable AI solutions on Google Cloud Platform. - Manage production deployments using Vertex AI, Cloud Run, GKE, Cloud Functions. - Configure Load Balancers, SSL Certificates, API Gateways, and Cloud Security Controls. - Collaborate with engineering teams to ensure reliable and secure deployments. Experience Requirements: - 7+ years of overall experience in Data Science, Machine Learning, and AI Engineering. - Minimum 5 years of hands-on experience in Machine Learning, MLOps, AIOps, Model Deployment & Production Support. - Minimum 2 years of experience in Generative AI, Large Language Models (LLMs), RAG-based Applications, and AI Agent Development. - Proven experience delivering AI solutions from concept to production. This job requires a seasoned professional who can contribute significantly to the development and deployment of advanced AI solutions while ensuring scalability, reliability, and security. As a Data Science & GenAI Specialist (AI/ML Engineer) GCP, your role involves leveraging your expertise in Machine Learning, MLOps/AIOps, Generative AI, and Google Cloud Platform (GCP) to design, develop, and deploy enterprise-scale AI solutions. You will be responsible for implementing various AI solutions, including LLM-powered applications, AI Agents, RAG architectures, and machine learning models in production environments. Key Responsibilities: - Generative AI & LLM Solutions - Design, develop, and deploy enterprise-grade Generative AI applications using Google Gemini models. - Build and optimize Retrieval-Augmented Generation (RAG) architectures using enterprise knowledge sources. - Develop AI-powered assistants, copilots, and intelligent automation solutions. - Implement prompt engineering, prompt optimization, and evaluation frameworks. - Fine-tune and customize foundation models where required. - AI Agent Development - Design and implement AI Agents using Agent Development Kit (ADK) and Agent-to-Agent Protocol (A2A). - Deploy and manage AI Agents on GCP infrastructure. - Build multi-agent orchestration frameworks for enterprise use cases. - Data Science & Machine Learning - Perform exploratory data analysis (EDA), feature engineering, model training, validation, and deployment. - Build predictive and prescriptive analytics solutions. - Develop and maintain ML pipelines supporting business-critical applications. - Apply advanced statistical and machine learning techniques to solve complex business problems. - MLOps & AIOps - Build scalable MLOps pipelines for model training, deployment, monitoring, and governance. - Implement model observability, drift detection, and performance monitoring. - Automate AI/ML lifecycle management through CI/CD pipelines. - Drive AIOps initiatives to improve operational efficiency and reliability. - Cloud & Production Deployment - Design and deploy scalable AI solutions on Google Cloud Platform. - Manage production deployments using Vertex AI, Clo