AI Engineer

As an AI Engineer, you will play a critical role in leveraging data science to drive significant business impact. You will lead the development of Agentic AI applications and traditional ML models, building intelligent systems that can reason through complex scenarios while also providing predictive and prescriptive insights.

You will be responsible for architecting solutions that are not only intelligent but safe, observable, and robust. You will bridge the gap between modern Large Language Models (LLMs) and traditional statistical techniques, ensuring that AI agents operate within strict guardrails and are backed by rigorous data analysis.

• 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.

• 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).