AI Integration Engineer, AI/ML

Advanced proficiency in Python is required for AI/ML backend development, data analysis, and API construction.

You must possess a deep understanding of prompt engineering strategies (Chain-of-Thought, ReAct) and context engineering to maximize model reasoning capabilities.

The position demands deep hands-on experience with Google ADK, MCP, Vertex AI Pipelines, and the Google RAG Engine.

You should have practical experience applying classical ML algorithms for regression, forecasting, and anomaly detection on large-scale datasets.

The role requires a strong understanding of vector databases and the manipulation of video/text embeddings for semantic search and classification.

Deep experience with utilizing GitHub Actions to support the SDLC workflows and CI/CD standards established by the Principal Architect.

You will transform the Principal Architect’s reference architectures into scalable, deployable code using Python, GKE, and Cloud Run.

Your primary focus involves building out agentic ecosystems using Google ADK (Agent Development Kit) and Model Context Protocol (MCP), specifically focusing on the Agent-to-Agent (A2A) orchestration patterns defined by the Architect.

You will apply advanced prompt engineering and context engineering techniques to optimize LLM performance, ensuring agents maintain state and accuracy over long interactions.

You will develop and tune high-fidelity RAG pipelines using the Google RAG Engine and Agent Engine to ensure agents have the precise context required for influencer marketing workflows.

The role requires applying machine learning techniques to our massive datasets to implement automated anomaly detection, forecasting, and trend analysis.

You will perform advanced data science and analysis by generating and querying high-dimensional video and text embeddings to extract insights from social media content.