Overview

Curious about the role? What your typical day would look like?

Responsibilities

• Design, develop, and deploy Generative AI and Agentic AI applications on GCP.
• Build AI agents and agentic workflows using Google ADK and Agent Engine.
• Work with Vertex AI and Gemini for LLM-based application development.
• Develop RAG-based solutions, including retrieval, grounding, context management, and prompt engineering.
• Implement GenAI evaluation frameworks to assess response quality, accuracy, relevance, groundedness, and overall application performance.
• Build scalable AI applications using Python and integrate enterprise data and APIs.
• Work with GCP services including GCS, BigQuery, Cloud Run, and Document AI.
• Use MLflow for experiment tracking, model management, and ML/GenAI lifecycle management.
• Support production deployment, monitoring, troubleshooting, and optimization of GenAI solutions.
• Collaborate with Data Scientists, Architects, Engineers, Product teams, and business stakeholders to translate requirements into scalable AI solutions.
• Participate in Agile development practices, sprint planning, reviews, and retrospectives.

Qualifications

• Solid experience in Generative AI / Machine Learning Engineering.
• Hands-on experience with GCP and Vertex AI.
• Strong knowledge of Google ADK, Gemini, and Agent Engine.
• Experience building RAG applications and AI agents.
• Solid understanding of Prompt Engineering and GenAI Evaluation.
• Proficiency in Python.
• Experience with MLflow.
• Hands-on experience with GCS, BigQuery, Cloud Run, and Document AI.
• Good understanding of production deployment and cloud-based AI solutions.
• Strong communication, stakeholder management, and collaboration skills.
• Experience working in an Agile workplace.
• Experience with AWS AI/ML services, particularly:
• AWS SageMaker
• Amazon Bedrock
• Experience with multi-cloud AI/ML environments.
• Exposure to GenAI monitoring, observability, governance, and production optimization.