Lead Machine Learning Engineer

AI/ML Engineering LeadAll roles are hybrid in the office (2-3 days/week) unless remote is noted. Please submit qualified candidates and include full legal name as it appears on the passport.Locations: Iselin, NJ, Charlotte, NC, or Chandler, AZDuration: 12+ months with possibility to convertHybrid onsite 2 days / 3 days remoteRequired 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.Include with submits please:Please provide your years of experience in the following:GenAI orchestration toolsLangGraphLangChainReACT, LLMConversational AIGoogle CloudMLOps practicesML frameworksReasonableAI (RAI)