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Position: AI Platform Engineer, Developer / AI Ops
Location: Charlotte, North Carolina
Work Arrangement: 100% Onsite
Experience: 7+ years in software or AI development
Primary Technology Stack: Google Cloud Platform, Google GECX Platform, Dialogflow, Agentic AI, Conversational AI, Python, FastAPI, Flask, Cloud Run, Cloud Scheduler, Secret Manager, REST APIs, AI Ops
Overview
The AI Platform Engineer 2 – Agentic AI Developer will design, develop, deploy, and support enterprise-grade Agentic AI and Conversational AI solutions using Google Cloud Platform and its associated AI services. This position combines advanced AI application development with hands-on cloud engineering, backend API development, production deployment, and AI operations.
The engineer will be responsible for creating intelligent agents and conversational experiences using Google GECX Platform and Dialogflow AI, integrating these capabilities with enterprise applications and client systems, and ensuring that deployed solutions are secure, scalable, reliable, and maintainable.
Working within an AI Ops delivery framework, the engineer will collaborate with data scientists, cloud architects, program managers, and other technical stakeholders to translate complex business requirements into production-ready AI solutions. The role requires strong independent problem-solving, practical engineering judgment, and the ability to communicate technical decisions to both technical and non-technical audiences.
The successful candidate will demonstrate the ability to take an AI use case from requirements through architecture, development, integration, deployment, monitoring, and continuous improvement, with measurable results for business users and clients.
Proposition
Purpose: Build intelligent AI solutions that solve meaningful business problems by combining autonomous agents, conversational interfaces, and enterprise cloud technologies. Your work will directly influence how organizations automate complex interactions, improve service delivery, and integrate AI into operational processes.
Growth: Deepen your expertise in Agentic AI, Conversational AI, Google Cloud AI services, cloud-native application engineering, and production AI operations. The role provides opportunities to work across the complete AI application lifecycle, contribute to emerging engineering practices, and help shape reusable delivery standards within a global technology organization.
Motivators: This opportunity is suited to an engineer who enjoys solving difficult technical problems, building real-world AI applications, and taking ownership of production outcomes rather than stopping at demonstrations or proofs of concept. The position combines hands-on Python development, Google Cloud engineering, intelligent-agent design, and collaboration with multidisciplinary teams in a fast-evolving technology environment.
Performance Objectives
1. Design and Deliver Production-Ready Agentic AI and Conversational AI Solutions. Within the first 30–90 days, assess assigned business use cases and design intelligent agent or conversational solutions using Google GECX Platform, Dialogflow AI, and appropriate Google Cloud AI capabilities. Develop working applications that can interpret user requests, execute approved workflows, interact with required services, and produce responses or actions aligned with business requirements. Implement appropriate conversation handling, agent orchestration, error recovery, and safeguards to ensure predictable and controlled behavior in the client environment. Success will be measured by stakeholder acceptance of delivered use cases, successful completion of agreed interaction scenarios, response quality, and achievement of defined functional and operational acceptance criteria.
2. Engineer Secure, Scalable AI Applications on Google Cloud Platform. Within the first 60–120 days, build and deploy production-grade AI application components using GCP services such as Cloud Run, Cloud Scheduler, and Secret Manager. Develop Python-based backend services and RESTful APIs using FastAPI or Flask to connect AI capabilities with enterprise applications, business workflows, and external systems. Apply approved authentication, configuration, secrets management, logging, deployment, and reliability practices to support secure and maintainable operations. Success will be measured by successful deployment of assigned services, validated API functionality, compliance with agreed security requirements, and achievement of application performance and availability targets.
3. Establish Reliable AI Ops Monitoring, Automation, and Lifecycle Practices. Within the first 90–150 days, implement or enhance the operational capabilities needed to monitor, troubleshoot, and maintain assigned AI applications and agent workflows. Develop appropriate logging, health checks, performance monitoring, automated scheduling, deployment controls, and incident investigation practices within the organization's GCP environment. Collaborate with architects and operational teams to identify failure conditions, manage dependencies, and improve the reliability of AI services after deployment. Success will be measured by visibility into application health, timely identification and resolution of incidents, successful execution of automated workflows, and achievement of agreed operational reliability measures.
4. Deliver Integrated AI Solutions and Advance Engineering Standards. Within the first 90–180 days, independently own assigned AI engineering deliverables while collaborating with data scientists, architects, program managers, and business stakeholders across the delivery lifecycle. Translate complex requirements into maintainable technical designs, integrate AI services with enterprise systems, and communicate architecture decisions, implementation risks, and results clearly. Evaluate emerging Google Cloud AI capabilities and contribute validated improvements, reusable components, and practical lessons to the organization's AI Ops engineering standards. Success will be measured by successful completion of assigned deliverables, stakeholder acceptance, reduced avoidable rework, and adoption of useful engineering improvements by the delivery team.
Critical Subtasks
1. Assess AI Use Cases, Cloud Architecture, and Delivery Requirements. During the first 15–30 days, review the assigned Agentic AI and Conversational AI use cases, existing GCP architecture, integration dependencies, security requirements, and AI Ops delivery practices. Evaluate the available Google GECX Platform and Dialogflow AI capabilities against the assigned requirements and identify gaps requiring additional engineering.
2. Build and Validate Intelligent Agents and Conversational Workflows. During the first 30–60 days, implement assigned intelligent-agent and conversational workflows using Google GECX Platform, Dialogflow AI, and appropriate supporting AI services. Develop conversation flows, approved agent actions, integration logic, and exception-handling mechanisms to support defined business scenarios. Test representative user interactions, including expected paths, ambiguous requests, unsupported actions, and failure conditions, to confirm that the solution behaves within agreed boundaries.
3. Develop Python Backend Services and Enterprise APIs. During the first 45–90 days, create or enhance backend services and RESTful APIs using Python, FastAPI, or Flask to support agent execution and enterprise integration. Implement appropriate request validation, service authentication, exception handling, logging, and data exchange mechanisms in accordance with approved engineering standards. Integrate the APIs with designated GCP services and client systems, ensuring that AI components can securely invoke required business functions.
4. Deploy Secure and Scalable AI Services on GCP. During the first 60–120 days, deploy assigned AI applications and backend services using Google Cloud Run and other approved GCP infrastructure components. Configure scheduled processing with Cloud Scheduler where appropriate and manage application credentials through Google Cloud Secret Manager and approved access controls. Validate deployment configurations, service connectivity, scalability behavior, and recovery procedures in the designated environments.
5. Implement AI Ops Monitoring, Testing, and Troubleshooting. During the first 90–150 days, establish or enhance monitoring and operational support for assigned AI agents, APIs, and cloud services. Implement appropriate application logs, health checks, alerts, workflow monitoring, and validation procedures to identify failures, performance degradation, or unexpected agent behavior. Investigate incidents systematically and coordinate corrective actions with architecture, development, and operations teams.
6. Integrate, Document, and Transfer Production AI Solutions. During the first 120–180 days, complete assigned integrations between AI services, enterprise applications, and client workflows while maintaining clear technical documentation. Collaborate with cross-functional teams to verify business acceptance, address implementation issues, and prepare support teams to operate delivered solutions. Share reusable implementation patterns, lessons learned, and practical engineering recommendations with colleagues working within the AI Ops delivery framework.
7. Continuously Evaluate and Integrate AI to Improve Performance. Within the first 90–180 days, take ownership of identifying how emerging AI capabilities and automation tools can support or enhance the core responsibilities of this role. Evaluate new Google Cloud AI/ML services, Agentic AI capabilities, and approved AI-assisted development tools for improving agent functionality, code quality, testing, monitoring, troubleshooting, and delivery efficiency. Lead appropriately scoped pilots, validate technical and business results, and incorporate proven improvements into daily engineering practices and shared delivery standards.
Critical Candidate Requirements
Location: Charlotte, North Carolina
Work Arrangement: 100% Onsite
Experience: 7+ years in software or AI development
Primary Technology Stack: Google Cloud Platform, Google GECX Platform, Dialogflow, Agentic AI, Conversational AI, Python, FastAPI, Flask, Cloud Run, Cloud Scheduler, Secret Manager, REST APIs, AI Ops
Overview
The AI Platform Engineer 2 – Agentic AI Developer will design, develop, deploy, and support enterprise-grade Agentic AI and Conversational AI solutions using Google Cloud Platform and its associated AI services. This position combines advanced AI application development with hands-on cloud engineering, backend API development, production deployment, and AI operations.
The engineer will be responsible for creating intelligent agents and conversational experiences using Google GECX Platform and Dialogflow AI, integrating these capabilities with enterprise applications and client systems, and ensuring that deployed solutions are secure, scalable, reliable, and maintainable.
Working within an AI Ops delivery framework, the engineer will collaborate with data scientists, cloud architects, program managers, and other technical stakeholders to translate complex business requirements into production-ready AI solutions. The role requires strong independent problem-solving, practical engineering judgment, and the ability to communicate technical decisions to both technical and non-technical audiences.
The successful candidate will demonstrate the ability to take an AI use case from requirements through architecture, development, integration, deployment, monitoring, and continuous improvement, with measurable results for business users and clients.
Proposition
Purpose: Build intelligent AI solutions that solve meaningful business problems by combining autonomous agents, conversational interfaces, and enterprise cloud technologies. Your work will directly influence how organizations automate complex interactions, improve service delivery, and integrate AI into operational processes.
Growth: Deepen your expertise in Agentic AI, Conversational AI, Google Cloud AI services, cloud-native application engineering, and production AI operations. The role provides opportunities to work across the complete AI application lifecycle, contribute to emerging engineering practices, and help shape reusable delivery standards within a global technology organization.
Motivators: This opportunity is suited to an engineer who enjoys solving difficult technical problems, building real-world AI applications, and taking ownership of production outcomes rather than stopping at demonstrations or proofs of concept. The position combines hands-on Python development, Google Cloud engineering, intelligent-agent design, and collaboration with multidisciplinary teams in a fast-evolving technology environment.
Performance Objectives
1. Design and Deliver Production-Ready Agentic AI and Conversational AI Solutions. Within the first 30–90 days, assess assigned business use cases and design intelligent agent or conversational solutions using Google GECX Platform, Dialogflow AI, and appropriate Google Cloud AI capabilities. Develop working applications that can interpret user requests, execute approved workflows, interact with required services, and produce responses or actions aligned with business requirements. Implement appropriate conversation handling, agent orchestration, error recovery, and safeguards to ensure predictable and controlled behavior in the client environment. Success will be measured by stakeholder acceptance of delivered use cases, successful completion of agreed interaction scenarios, response quality, and achievement of defined functional and operational acceptance criteria.
2. Engineer Secure, Scalable AI Applications on Google Cloud Platform. Within the first 60–120 days, build and deploy production-grade AI application components using GCP services such as Cloud Run, Cloud Scheduler, and Secret Manager. Develop Python-based backend services and RESTful APIs using FastAPI or Flask to connect AI capabilities with enterprise applications, business workflows, and external systems. Apply approved authentication, configuration, secrets management, logging, deployment, and reliability practices to support secure and maintainable operations. Success will be measured by successful deployment of assigned services, validated API functionality, compliance with agreed security requirements, and achievement of application performance and availability targets.
3. Establish Reliable AI Ops Monitoring, Automation, and Lifecycle Practices. Within the first 90–150 days, implement or enhance the operational capabilities needed to monitor, troubleshoot, and maintain assigned AI applications and agent workflows. Develop appropriate logging, health checks, performance monitoring, automated scheduling, deployment controls, and incident investigation practices within the organization's GCP environment. Collaborate with architects and operational teams to identify failure conditions, manage dependencies, and improve the reliability of AI services after deployment. Success will be measured by visibility into application health, timely identification and resolution of incidents, successful execution of automated workflows, and achievement of agreed operational reliability measures.
4. Deliver Integrated AI Solutions and Advance Engineering Standards. Within the first 90–180 days, independently own assigned AI engineering deliverables while collaborating with data scientists, architects, program managers, and business stakeholders across the delivery lifecycle. Translate complex requirements into maintainable technical designs, integrate AI services with enterprise systems, and communicate architecture decisions, implementation risks, and results clearly. Evaluate emerging Google Cloud AI capabilities and contribute validated improvements, reusable components, and practical lessons to the organization's AI Ops engineering standards. Success will be measured by successful completion of assigned deliverables, stakeholder acceptance, reduced avoidable rework, and adoption of useful engineering improvements by the delivery team.
Critical Subtasks
1. Assess AI Use Cases, Cloud Architecture, and Delivery Requirements. During the first 15–30 days, review the assigned Agentic AI and Conversational AI use cases, existing GCP architecture, integration dependencies, security requirements, and AI Ops delivery practices. Evaluate the available Google GECX Platform and Dialogflow AI capabilities against the assigned requirements and identify gaps requiring additional engineering.
2. Build and Validate Intelligent Agents and Conversational Workflows. During the first 30–60 days, implement assigned intelligent-agent and conversational workflows using Google GECX Platform, Dialogflow AI, and appropriate supporting AI services. Develop conversation flows, approved agent actions, integration logic, and exception-handling mechanisms to support defined business scenarios. Test representative user interactions, including expected paths, ambiguous requests, unsupported actions, and failure conditions, to confirm that the solution behaves within agreed boundaries.
3. Develop Python Backend Services and Enterprise APIs. During the first 45–90 days, create or enhance backend services and RESTful APIs using Python, FastAPI, or Flask to support agent execution and enterprise integration. Implement appropriate request validation, service authentication, exception handling, logging, and data exchange mechanisms in accordance with approved engineering standards. Integrate the APIs with designated GCP services and client systems, ensuring that AI components can securely invoke required business functions.
4. Deploy Secure and Scalable AI Services on GCP. During the first 60–120 days, deploy assigned AI applications and backend services using Google Cloud Run and other approved GCP infrastructure components. Configure scheduled processing with Cloud Scheduler where appropriate and manage application credentials through Google Cloud Secret Manager and approved access controls. Validate deployment configurations, service connectivity, scalability behavior, and recovery procedures in the designated environments.
5. Implement AI Ops Monitoring, Testing, and Troubleshooting. During the first 90–150 days, establish or enhance monitoring and operational support for assigned AI agents, APIs, and cloud services. Implement appropriate application logs, health checks, alerts, workflow monitoring, and validation procedures to identify failures, performance degradation, or unexpected agent behavior. Investigate incidents systematically and coordinate corrective actions with architecture, development, and operations teams.
6. Integrate, Document, and Transfer Production AI Solutions. During the first 120–180 days, complete assigned integrations between AI services, enterprise applications, and client workflows while maintaining clear technical documentation. Collaborate with cross-functional teams to verify business acceptance, address implementation issues, and prepare support teams to operate delivered solutions. Share reusable implementation patterns, lessons learned, and practical engineering recommendations with colleagues working within the AI Ops delivery framework.
7. Continuously Evaluate and Integrate AI to Improve Performance. Within the first 90–180 days, take ownership of identifying how emerging AI capabilities and automation tools can support or enhance the core responsibilities of this role. Evaluate new Google Cloud AI/ML services, Agentic AI capabilities, and approved AI-assisted development tools for improving agent functionality, code quality, testing, monitoring, troubleshooting, and delivery efficiency. Lead appropriately scoped pilots, validate technical and business results, and incorporate proven improvements into daily engineering practices and shared delivery standards.
Critical Candidate Requirements
- Professional Experience: At least seven years of software or AI development experience, with demonstrated delivery of cloud-based solutions involving Agentic AI, Conversational AI, or NLP technologies.
- Agentic AI and Conversational AI: Strong practical experience designing and implementing intelligent agents, conversational workflows, and AI-driven application functionality. Experience with Google GECX Platform and Dialogflow AI is specifically requested.
- Google Cloud Platform: Deep hands-on experience with GCP, including Cloud Run, Cloud Scheduler, Secret Manager, and cloud-native application deployment.
- Python and API Engineering: Strong Python programming skills and production experience developing RESTful APIs or microservices using FastAPI, Flask, or comparable frameworks.
- AI Ops and Production Operations: Understanding of AI application deployment, monitoring, automation, troubleshooting, and lifecycle management, including the ability to support end-to-end AI application workflows.
- System Integration: Experience integrating AI applications, backend services, and cloud infrastructure with enterprise systems and business workflows.
- Engineering Quality and Security: Ability to design secure, maintainable, scalable solutions and apply appropriate testing, logging, configuration, and operational controls.
- Cross-Functional Collaboration: Ability to work effectively with data scientists, architects, program managers, and technical and non-technical stakeholders, including colleagues across global teams.
- Problem-Solving and Communication: Demonstrated ability to analyze complex technical issues, communicate architectural decisions, and independently deliver effective engineering solutions.
- Worksite Requirement: Ability to work onsite in Charlotte, North Carolina, as required by the position.
- The ideal candidate is an experienced AI and cloud application engineer with at least seven years of relevant software development experience and a strong portfolio of delivered Agentic AI, Conversational AI, or NLP-based solutions.
- This individual has personally designed and deployed AI applications on Google Cloud Platform and can demonstrate strong proficiency with Python, FastAPI or Flask, cloud-native services, and enterprise integrations.
- The strongest candidates will combine expertise in Google GECX Platform and Dialogflow AI with practical experience building secure, scalable production applications using Cloud Run, Cloud Scheduler, and Secret Manager.
- They will also understand the operational challenges associated with AI systems, including monitoring agent behavior, handling failed integrations, investigating unexpected responses, managing deployments, and maintaining application reliability.
- Candidates should be evaluated on their demonstrated ownership of real production solutions rather than their familiarity with AI terminology alone.
- During the interview, ask candidates to describe a significant Agentic AI or Conversational AI solution they personally delivered. They should explain the business problem, agent architecture, Google Cloud services used, Python/API integrations, deployment approach, operational challenges, and measurable business results.
- The primary differentiator is the ability to take an intelligent-agent solution from concept through secure production deployment and ongoing operational support.