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Chatbot Manager Jobs in California (NOW HIRING)

Kikoff

CX Strategy Manager

San Francisco, CA · On-site

Own chatbot and AI automation strategy (coverage, containment, escalation quality) * Expand ... Hire, manage, and develop CX Strategy Associates * Partner x-functionally to improve efficiency and ...

Forhyre

Test Automation - Manager

Los Angeles, CA · On-site

$49.50 - $65.50/hr

As the Test Automation Manager, you will be responsible for leading and managing our automation ... C#, Chatbot Testing, ComputerVision Testing, Cypress, Katalon, MBT, ML/NLP Testing, PTF, RPA ...

Forhyre

Test Automation - Manager

San Francisco, CA

$54.25 - $71.50/hr

As the Test Automation Manager, you will be responsible for leading and managing our automation ... C#, Chatbot Testing, ComputerVision Testing, Cypress, Katalon, MBT, ML/NLP Testing, PTF, RPA ...

Forhyre

Test Automation - Manager

Sunnyvale, CA · On-site

$54 - $71.25/hr

As the Test Automation Manager, you will be responsible for leading and managing our automation ... C#, Chatbot Testing, ComputerVision Testing, Cypress, Katalon, MBT, ML/NLP Testing, PTF, RPA ...

Kikoff

CX Strategy Manager

San Francisco, CA · On-site

$175K - $200K/yr

Own chatbot and AI automation strategy (coverage, containment, escalation quality) * Expand ... Hire, manage, and develop CX Strategy Associates * Partner x-functionally to improve efficiency and ...

EBizCharge

Technical Writer I

Irvine, CA · On-site

Test and verify the AI chatbot feature to help ensure it returns accurate, helpful, and relevant ... Manage multiple documentation assignments across product releases and ongoing updates.

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Chatbot Manager information

What is the difference between Chatbot Manager vs Chatbot Developer?

AspectChatbot ManagerChatbot Developer
Primary RoleOversees chatbot projects, manages teams, and ensures chatbot performance aligns with business goals.Designs, codes, and implements chatbot functionalities and features.
Required SkillsProject management, communication, understanding of AI and NLP concepts.Programming languages (Python, JavaScript), AI/NLP development skills.
Work EnvironmentTypically in managerial or coordination roles within tech or customer service industries.Hands-on coding and development in software or AI teams.
Common CertificationsProject management certifications, AI/NLP courses.Programming certifications, AI/NLP training.

While a Chatbot Manager oversees the overall chatbot strategy and team, a Chatbot Developer focuses on building and coding the chatbot functionalities. Both roles often collaborate but differ in their core responsibilities and skill sets.

What is a Chatbot Manager?

A Chatbot Manager is a professional responsible for overseeing the development, deployment, and optimization of chatbots for organizations. They coordinate between technical teams, content creators, and stakeholders to ensure chatbots provide effective customer service, support, or engagement. Their duties include analyzing chatbot performance, managing updates, and implementing improvements based on user feedback and analytics. Chatbot Managers also stay updated with the latest AI and conversational technologies to enhance user experience.

What are some common challenges faced by a Chatbot Manager when optimizing bot performance, and how are they typically addressed?

A Chatbot Manager often encounters challenges such as ensuring the chatbot accurately understands user intent, maintaining up-to-date responses, and handling complex or ambiguous queries. To address these, managers regularly analyze chat logs for patterns, work closely with data analysts and conversational designers to refine scripts, and implement continuous training for the chatbot using real user data. Collaborating with customer support and product teams helps ensure the bot remains aligned with evolving business needs and delivers consistent, high-quality user experiences.

What are the key skills and qualifications needed to thrive as a Chatbot Manager, and why are they important?

To thrive as a Chatbot Manager, you need expertise in conversational design, data analysis, and a solid understanding of AI and natural language processing, often backed by a degree in computer science or a related field. Familiarity with chatbot platforms (like Dialogflow or Microsoft Bot Framework), analytics tools, and experience with APIs or scripting languages is typically required. Strong communication, problem-solving, and project management skills help you collaborate across teams and optimize user experiences. These skills are crucial for developing effective, user-friendly chatbots that drive customer engagement and achieve business objectives.
What are the most commonly searched types of Chatbot jobs in California? The most popular types of Chatbot jobs in California are:
What are popular job titles related to Chatbot Manager jobs in California? For Chatbot Manager jobs in California, the most frequently searched job titles are:
What job categories do people searching Chatbot Manager jobs in California look for? The top searched job categories for Chatbot Manager jobs in California are:
What cities in California are hiring for Chatbot Manager jobs? Cities in California with the most Chatbot Manager job openings:
Chatbot Manager jobs near you
Infographic showing various Chatbot Manager job openings in California as of July 2026, with employment types broken down into 54% As Needed, 20% Full Time, 4% Part Time, 1% Temporary, 19% Nights, and 2% Summer. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution.
Chatbot Developer

Chatbot Developer

Software People, Inc.

Los Angeles, CA • On-site

Other

Re-posted 7 days ago

Job description

Chatbot Developer

Location: Los Angeles, CA

Duration: 12+ months

Phone/Skype Hire. Onsite from day 1

Responsibilities

  • Design and develop NLU application (chatbot) using Lex. Design Conversational Flow: Create a flowchart or diagram outlining the chatbot's conversation paths, responses, and possible user inputs.
  • Develop Natural Language Processing (NLP) Capabilities: Implement NLP models to enable the chatbot to understand and process user inputs effectively. Create intents and define slot types based on the application requirement. Set up how the lex bot conversation should flow and error handling. Use Lex's dialog management system to specify prompts for each slot and determine fallback responses. Use Lambda functions for dynamic responses or backend logic.
  • Thoroughly test the bot to ensure it responds accurately, handles edge cases, and performs smoothly in various situations. Use the built-in testing interface in the AWS Lex console to simulate conversations. Implement monitoring using Amazon CloudWatch to track bot performance and errors. Adjust intents, prompts, and Lambda functions as needed based on user feedback.
  • Use APIs to interact with different LLMs. Understand various LLMs available on Amazon Bedrock and how to invoke them efficiently and cost-optimized using customized completion prompts.
  • Use various AWS technologies like API G/W, Lambda, S3, Cloud Formation scripts, CloudWatch, etc to create a complete solution that can be monitored, optimized, and updated with automation tools.
  • Write and Optimize Python Code. Develop clean, readable, and efficient Python code to solve problems or build applications. Continuously refactor code for readability and performance. Use libraries like collections, lang chain, and built-in functions to optimize code.
  • Develop services using SOA and interact with APIs to send or receive data from external systems or services using libraries to make GET, POST, PUT, and DELETE requests. Create and process JSON requests and responses and manage authentication (like OAuth tokens or API keys).
  • Manage project dependencies and keep environments isolated for each project. Create application packages and docker images that can be deployed in a cloud infrastructure. Ensure all dependencies are packaged and use venv or virtualenv to create isolated Python environments for different projects.
  • Ensure code quality by writing tests and debugging issues that arise. Conduct thorough testing, including unit testing and user acceptance testing (UAT), to ensure the chatbot functions as expected and fix any bugs.
  • Use database concepts to write and create SQL queries to extract data from the database.