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Google Conversational Ai Jobs (NOW HIRING)

RIT Solutions

AI/ML Engineering Lead

Iselin, NJ · Hybrid

$104K - $137K/yr

Proven experience in Conversational AI and synthetic agent development, especially within Google Cloud environments. * Hands-on expertise with GenAI orchestration tools (LangGraph, LangChain, ReACT ...

RIT Solutions, Inc.

AI/ML Engineering Lead

Glendale, CA · Hybrid

$108K - $143K/yr

Proven experience in Conversational AI and synthetic agent development, especially within Google Cloud environments. * Hands-on expertise with GenAI orchestration tools (LangGraph, LangChain, ReACT ...

RIT Solutions

AI/ML Engineering Lead

Chandler, AZ · Hybrid

$104K - $137K/yr

Proven experience in Conversational AI and synthetic agent development, especially within Google Cloud environments. * Hands-on expertise with GenAI orchestration tools (LangGraph, LangChain, ReACT ...

Forhyre

Tech Lead - Conversational AI

Plano, TX

As a Tech Lead - Conversational AI, you will play a crucial role in developing and implementing ... Proficiency in working with cloud platforms like AWS, Azure, or Google Cloud Platform.

Forhyre

OH

As a Tech Lead - Conversational AI, you will play a crucial role in developing and implementing ... Proficiency in working with cloud platforms like AWS, Azure, or Google Cloud Platform.

Forhyre

Tech Lead - Conversational AI

Dallas, TX

As a Tech Lead - Conversational AI, you will play a crucial role in developing and implementing ... Proficiency in working with cloud platforms like AWS, Azure, or Google Cloud Platform.

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Google Conversational Ai information

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How much do google conversational ai jobs pay per hour?

As of Jun 16, 2026, the average hourly pay for google conversational ai in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is a Google Conversational AI specialist?

A Google Conversational AI specialist is a professional who designs, develops, and manages AI-driven conversational interfaces using Google technologies such as Dialogflow, Google Cloud Natural Language API, and Google Assistant. They work to create chatbots, virtual assistants, and other automated systems that can understand and respond to human language naturally. Their responsibilities often include integrating these solutions into websites, apps, or customer service platforms, as well as optimizing the AI for improved accuracy and user experience.

What is the difference between Google Conversational Ai vs Chatbot Developer?

AspectGoogle Conversational AiChatbot Developer
Primary FocusDesigning and building conversational AI systems using Google's toolsCreating and programming chatbots for various platforms
Required SkillsNatural language processing, machine learning, API integrationProgramming languages (Python, JavaScript), chatbot frameworks
Work EnvironmentTech companies, AI development teams, cloud platformsCustomer service, marketing, or tech firms developing chatbots
CertificationsGoogle Cloud certifications, AI/ML coursesProgramming certifications, platform-specific training

Google Conversational Ai focuses on developing advanced conversational systems using Google's AI tools, while Chatbot Developers primarily build and program chatbots across various platforms. Both roles require programming and AI knowledge but differ in scope and tools used.

What are the key skills and qualifications needed to thrive as a Google Conversational AI Engineer, and why are they important?

To thrive as a Google Conversational AI Engineer, you need strong programming skills (Python, Java), expertise in natural language processing (NLP), and a solid foundation in machine learning, typically backed by a degree in computer science or a related field. Familiarity with tools such as TensorFlow, Dialogflow, Google Cloud Platform, and relevant AI/ML frameworks is essential. Excellent problem-solving abilities, creativity, and effective communication are important soft skills for collaborating with diverse teams and interpreting user needs. These competencies enable the development of intuitive, accurate conversational agents that drive user satisfaction and business value.

How does a professional working in Google Conversational AI typically collaborate with cross-functional teams to develop and improve AI-driven products?

Professionals in Google Conversational AI regularly collaborate with engineers, product managers, UX designers, linguists, and data scientists to design, test, and refine conversational interfaces. This teamwork ensures that AI models are not only technically robust but also user-friendly and contextually accurate. A typical workflow involves frequent meetings for ideation, sprint planning, and reviewing user feedback, allowing for continuous iteration on features and capabilities. Such collaboration is essential for aligning technical solutions with user needs and business goals.
Infographic showing various Google Conversational Ai job openings in the United States as of June 2026, with employment types broken down into 6% Internship, 66% Full Time, 14% Part Time, and 14% Contract. Highlights an 71% In-person, 3% Hybrid, and 26% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.
AI/ML Engineering Lead

AI/ML Engineering Lead

RIT Solutions

Iselin, NJ • Hybrid

$104K - $137K/yr

Other

Posted 10 days ago

Job description

AI/ML Engineering Lead

All 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, AZ. Contract: 12+ months with possibility to convert. Hybrid onsite 2 days/3 days remote.

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.
Include With Submits Please

Please provide your years of experience in the following:

  • GenAI orchestration tools
  • LangGraph
  • LangChain
  • ReACT, LLM
  • Conversational AI
  • Google Cloud
  • MLOps practices
  • ML frameworks
  • ReasonableAI (RAI)

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