AI/ML Engineering Lead
Day to Day:
• Develop Synthetic AI Agents using the Google Conversational Platform and playbooks to enhance automated interactions.
• Orchestrate multiple Generative AI Agents using LangGraph, LangChain (with ReACT), and LLM tooling for intelligent workflow automation.
• Architect and implement large-scale, low-latency, real-time systems with a focus on event-driven processing and extended conversational context using Big Table, Time Series, Pub/Sub, and Kafka.
• Leverage ML frameworks and MLOps best practices to streamline the deployment, monitoring, and maintenance of AI models.
• Continuously combat AI hallucinations by implementing real-time detection and correction mechanisms, rather than one-time adjustments.
• Design and implement guardrails, supervisory mechanisms, and observability frameworks to ensure AI transparency, reliability, and explainability.
• Lead Responsible AI (RAI) initiatives at scale, ensuring compliance with regulatory requirements for industries like Fintech.
• Optimize cost-efficiency of GenAI solutions through hybrid approaches, balancing deterministic and probabilistic methods.
• Integrate AI solutions into Google Cloud's native microservices and event-driven architectures, leveraging technologies such as Big Table, Pub/Sub, and AlloyDB
Required Skillset:
• Proven experience in Conversational AI and synthetic agent development, especially within Google Cloud environments.
• Hands-on expertise with GenAI orchestration tools (LangGraph, LangChain, ReACT, LLMs).
• Strong background in real-time, event-driven architectures and cloud-native technologies (GCP, Kafka, Pub/Sub, Big Table).
• Deep understanding of MLOps practices for scalable AI deployment and monitoring.
• Experience in Responsible AI (RAI) and regulatory AI governance, especially in Fintech or other highly regulated industries.
• Track record of cost-efficient AI model deployment, optimizing deterministic vs. probabilistic approaches.
Day to Day:
• Develop Synthetic AI Agents using the Google Conversational Platform and playbooks to enhance automated interactions.
• Orchestrate multiple Generative AI Agents using LangGraph, LangChain (with ReACT), and LLM tooling for intelligent workflow automation.
• Architect and implement large-scale, low-latency, real-time systems with a focus on event-driven processing and extended conversational context using Big Table, Time Series, Pub/Sub, and Kafka.
• Leverage ML frameworks and MLOps best practices to streamline the deployment, monitoring, and maintenance of AI models.
• Continuously combat AI hallucinations by implementing real-time detection and correction mechanisms, rather than one-time adjustments.
• Design and implement guardrails, supervisory mechanisms, and observability frameworks to ensure AI transparency, reliability, and explainability.
• Lead Responsible AI (RAI) initiatives at scale, ensuring compliance with regulatory requirements for industries like Fintech.
• Optimize cost-efficiency of GenAI solutions through hybrid approaches, balancing deterministic and probabilistic methods.
• Integrate AI solutions into Google Cloud's native microservices and event-driven architectures, leveraging technologies such as Big Table, Pub/Sub, and AlloyDB
Required Skillset:
• Proven experience in Conversational AI and synthetic agent development, especially within Google Cloud environments.
• Hands-on expertise with GenAI orchestration tools (LangGraph, LangChain, ReACT, LLMs).
• Strong background in real-time, event-driven architectures and cloud-native technologies (GCP, Kafka, Pub/Sub, Big Table).
• Deep understanding of MLOps practices for scalable AI deployment and monitoring.
• Experience in Responsible AI (RAI) and regulatory AI governance, especially in Fintech or other highly regulated industries.
• Track record of cost-efficient AI model deployment, optimizing deterministic vs. probabilistic approaches.
