Python/ AI Developer (Remote)

as an AI Engineer, you will architect and deploy sophisticated AI agents and generative workflows to revolutionize our business operations. You will be at the forefront of building intelligent systems that automate complex backend processes and empower our users with cutting-edge AI-driven tools. Working closely with business stakeholders, you will translate enterprise challenges into scalable, production-grade AI solutions on the Google Cloud Platform.

• Work with business stakeholders to identify and understand business problems and architect AI-powered systems to address them.
• Design, build, and maintain sophisticated AI systems, including autonomous agents, generative workflows, and fine-tuned Large Language Models (LLMs).
• Partner with software engineering, product, and infrastructure teams to integrate AI capabilities into new and existing applications.
• Research, evaluate, and implement state-of-the-art AI technologies, models, and platforms to ensure our systems remain on the cutting edge.
• Own and operate the end-to-end MLOps lifecycle on Google Cloud Platform, including building data pipelines, automating model deployment (CI/CD), monitoring system performance, and ensuring scalability.

• 2+ years of professional experience in an AI Engineer, Machine Learning Engineer, or related software engineering role.
• Mastery of Python and its core AI/ML libraries. This includes strong experience with SQL and hands-on experience with vector databases (e.g., Agent Platform Vector Search) for RAG implementations.
• Demonstrated ability to build, fine-tune, and deploy production-grade Generative AI systems and multi-step agentic workflows from the ground up.
• Proven experience with modern agent frameworks, specifically Google's Agent Development Kit (ADK) or similar toolkits like LangChain or LlamaIndex.
• Strong proficiency in the GCP ecosystem, with direct experience deploying and managing AI solutions using Agent Platform. You have a demonstrable understanding of the challenges of deploying AI in production environments (e.g., model limitations, scalability, reliability)
• Experience designing and implementing evaluation frameworks to rigorously measure and compare the accuracy, latency, and cost of different Generative AI workflows and agentic systems.