Job Description

Roles and Responsibilities: GenAI Solution Design & Implementation: Architect and deploy scalable GenAI solutions, integrating emerging cloud technologies. Presales & Business Development: Identify and qualify GenAI business opportunities, develop Go-To-Market strategies, and lead presales engagements. Solution Architecture & Optimization: Analyze, document, and optimize GenAI architectures, creating SOWs and delivering technical presentations. Client & Team Leadership: Translate client needs into technical GenAI solutions, building strong relationships and providing architectural guidance. GenAI Expertise: Apply deep knowledge in computer vision, NLP, conversational AI, and MLOps, with a focus on LLM architecture and model lifecycle management. Platform Proficiency: Demonstrate expertise with Google AI and Data products (BigQuery, Document AI, CCAI, Dialogflow, Vertex AI, Dataprep) or equivalent cloud platform tools. Governance & Compliance: Establish lifecycle management processes for GenAI systems, ensuring ethical and regulatory compliance. Knowledge Sharing & Thought Leadership: Contribute to the GenAI community, develop white papers, and package best practices. Team Mentorship & Collaboration: Mentor teams, provide architectural leadership, and collaborate with internal and external stakeholders. Technical Requirements: Expertise in computer vision, NLP, conversational AI, and MLOps. Deep understanding of ML architecture and model lifecycle, including non-functional requirements. Proficiency with Google AI and Data products (BigQuery, Document AI, CCAI, Dialogflow, Vertex AI, Dataprep) or equivalent cloud platform tools. Familiarity with ethics, regulatory, and compliance factors in data and model management. Experience with LLM architecture. Qualification: 8-15 years of experience in delivering complex AI or Data Analytics projects on Cloud Platforms (Google and/or AWS) Experience in a statistical programming language (e.g. R or Python) and applied machine learning and AI techniques (i.e. computer vision, deep learning, conversational AI and natural language processing frameworks). Experience in systems design with the ability to architect and explain Machine Learning Operations platforms. Ability to translate business problems into futuristic AI solutions Excellent collaboration skills, experience working in client collaborative environments on Data-driven projects Proven success in contributing in a multi-geographical team Demonstrate excellent communication, written, presentation, and problem-solving skills.