AI Engineer

• Design and build state-of-the-art AI agents using modern orchestration frameworks (e.g., LangGraph, LangChain, Google ADK) to automate complex reasoning tasks.
• Design and implement supervised and unsupervised machine learning models (e.g., regression, classification, clustering) and statistical experiments to support data-driven decision-making.
• Develop advanced RAG (Retrieval-Augmented Generation) pipelines and API connectors to ingest and synthesize data from diverse sources, including internal databases, unstructured technical documents, and external third-party data.
• Implement robust safety layers and input/output validation using specialized frameworks (e.g., Google Model Armor) to prevent hallucinations, ensure data privacy, and maintain compliance.
• Build comprehensive monitoring pipelines using advanced evaluation tools (e.g., Arize Phoenix, Langfuse) to trace agent reasoning steps, track token usage, and monitor latency in production.
• Adopt rigorous evaluation frameworks (e.g., Ragas, GenAI Evaluation Service) to measure performance. Stay updated on research papers and cutting-edge algorithms in both the GenAI and ML domains.
• Develop and deploy AI solutions exclusively within the Cloud Platform (GCP/AWS/Azure) ecosystem, utilizing Vertex AI, Cloud Run, and BigQuery.

Qualifications

• 3+ years of professional experience in Data Science or Software Engineering, with a strong dual focus on Generative AI/LLM applications and traditional Machine Learning.
• Bachelor’s or Master’s degree in a quantitative field (e.g., Computer Science, AI, Statistics, Mathematics, or Engineering). A PhD is preferred.
• 2+ years of hands-on experience with supervised/unsupervised learning and statistical modeling.
• Proven experience building Agentic workflows (reasoning loops, tool use/function calling) rather than simple chatbots.
  • Deep understanding of the GCP stack for AI/Data (Vertex AI, BigQuery).