AI Development & Implementation Lead

Pexly is looking for an AI Development & Implementation Lead to turn AI into real, production-ready solutions for customer support operations.

You will personally build, integrate, test and deploy AI solutions across Chat, Email, Voice and Social Media — from rapid prototypes to scalable production systems.

Main Purpose of the Role

The AI Implementation Manager is responsible for developing, integrating, deploying, and continuously improving AI solutions across the customer support services delivered by our BPO organization, using the latest Vibe Coding potential.

The role covers AI implementation across all major customer interaction channels:

Chat | Email | Voice/Phone | Social Media

The objective is not simply to experiment with AI, but to turn AI into reliable, production-ready customer support operations that improve efficiency, quality, scalability, response times, customer experience, and cost.

As our customer support teams operate primarily within third-party CRM, ticketing, contact-center, telephony, social media, and other systems owned or hosted by our clients, a critical part of the role is integrating AI solutions into these existing environments.

The AI Implementation Manager combines AI expertise, automation, integration capabilities, vibe coding, operational understanding, and project ownership.

Key Responsibilities

AI Development & Implementation

• Identify customer-support processes and interactions suitable for AI automation or AI assistance.
• Design and deploy AI solutions for chat, email, voice/phone, and social media support.
• Develop both fully autonomous AI agents and AI copilots/assistants for human agents.
• Translate existing human-agent workflows, SOPs, knowledge bases, decision trees, and escalation procedures into AI-driven processes.
• Build prototypes rapidly and convert successful prototypes into stable production solutions.
• Continuously evaluate new AI models, platforms, frameworks, and technologies.
• Measure AI performance against human-agent and operational benchmarks.

CRM & Third-Party Platform Integration

A major responsibility of this position is connecting AI with the environments in which our clients operate.

• Integrate AI agents and automations with third-party CRM, helpdesk, contact-center, telephony, social media, and knowledge-management platforms.
• Work extensively with APIs, webhooks, MCP, middleware and automation platforms.
• Enable AI agents to securely retrieve and update information within client systems.
• Build workflows allowing AI to perform actions such as creating/updating tickets, retrieving customer information, categorizing interactions, updating CRM records, triggering escalations and handing conversations to human agents.
• Understand client system architecture and determine the most practical integration approach.
• Work with client IT/technical teams where access, API credentials, permissions, security requirements or custom integrations are required.
• Design integrations that minimize unnecessary changes to the client's existing technology environment.

AI Tech Stack

The AI Implementation Manager must have strong hands-on technical capabilities and be able to personally design, build, integrate, test and deploy AI solutions, rather than only managing external developers or technology providers.

Key capabilities include:

• Extensive experience with Vibe Coding & AI-Assisted Development, using tools such as Claude Code, OpenAI Codex and other leading AI development environments.
• Ability to rapidly turn business requirements and customer-support processes into working AI applications, agents, integrations and automations.
• Advanced practical knowledge of LLMs, AI agents and agentic workflows, including prompting, context management, tool use, structured outputs and human handovers.
• Strong experience building API-based integrations between AI solutions and third-party CRM, ticketing, telephony, social media and client systems.
• Ability to work with APIs, webhooks, authentication, databases, external data sources and modern integration protocols.
• Experience developing AI solutions that can securely retrieve information from and perform actions within client systems.
• Strong understanding of workflow and process automation, including multi-step processes involving AI, business systems and human approvals.
• Experience developing conversational AI for chat, email and social media interactions.
• Practical knowledge of Voice AI, including speech recognition, voice generation, real-time conversations, telephony integration and AI-to-human transfer.
• Ability to connect AI solutions with company knowledge, operational procedures, customer data and other relevant information sources.
• Experience with AI testing, evaluation and quality control, including accuracy, hallucination prevention, edge cases and escalation behavior.
• Ability to move AI solutions from prototype to reliable production deployment, including testing, debugging, monitoring, version control and continuous improvement.
• Good understanding of AI security, data privacy, access control and GDPR implications, particularly when AI interacts with customer data and client systems.
• Continuously research and test new AI models, coding environments, agent frameworks, integration methods and emerging technologies, replacing existing technology when better solutions become available.

Workflow Automation

• Identify repetitive activities currently performed manually by customer-support agents.
• Automate workflows using AI combined with automation/orchestration technologies.
• Build end-to-end workflows connecting LLMs, CRM systems, APIs, databases, knowledge bases and communication channels.
• Develop automated routing, classification, summarization, quality monitoring, escalation and reporting.
• Reduce unnecessary human involvement while maintaining appropriate human control.
• Design robust exception handling and fallback procedures.

The AI Implementation Manager will:

• Assess client knowledge bases, SOPs, FAQs and operational processes for AI readiness.
• Identify missing, contradictory, outdated or unclear information before deployment.
• Structure knowledge for effective AI retrieval and reasoning.
• Build and optimize RAG/knowledge-retrieval solutions where appropriate.
• Establish processes for continuously updating AI knowledge.
• Implement testing to detect incorrect, unsupported or hallucinated responses.

Testing, QA & Production Monitoring

• Build structured testing processes before AI solutions go live.
• Test accuracy, reliability, integrations, edge cases and escalation behavior.
• Monitor AI conversations and production performance.
• Establish measurable AI KPIs including automation rate, containment rate, resolution rate, accuracy, response time, CSAT and cost per interaction.
• Analyze AI failures and continuously improve prompts, workflows, knowledge and integrations.
• Implement logging, monitoring and alerting for production AI systems.

Security, Privacy & Compliance

• Ensure AI implementations follow client security requirements and applicable data-protection standards.
• Work with restricted API permissions and appropriate access controls.
• Understand GDPR and the implications of processing customer information through AI systems.
• Ensure sensitive customer information is handled appropriately.
• Work with internal Information Security and client IT/security teams before production deployments where required.

You will:

• Participate in client discovery meetings.
• Identify AI automation opportunities within existing support operations.
• Translate operational requirements into technical solutions.
• Estimate implementation complexity, resources and timelines.
• Demonstrate prototypes and proposed AI solutions to clients.
• Coordinate implementation with Operations, IT, QA and client stakeholders.
• Manage pilots and production rollouts.
• Document implementations and provide operational handover.
• Track measurable business results after deployment.

Required Experience & Skills

The ideal candidate has strong practical experience in several of the following areas:

• AI/LLM implementation in real production environments.
• Advanced vibe coding / AI-assisted software development.
• Extensive hands-on use of Claude/Claude Code and/or OpenAI Codex.
• Building AI agents and agentic workflows.
• API and webhook integrations.
• REST APIs, JSON, OAuth/API authentication and basic database concepts.
• CRM/helpdesk/contact-center integrations.
• Workflow automation and orchestration.
• Prompt engineering and context engineering.
• RAG and knowledge-base integrations.
• Voice AI and conversational AI.
• LLM evaluation, testing and monitoring.
• Git/version control and basic software-development practices.
• Understanding of information security and data privacy.
• Ability to understand existing business processes and translate them into automated workflows.

Experience with platforms such as Zendesk, Freshdesk, Salesforce, HubSpot, Intercom, ServiceNow, Dynamics 365, Genesys, Twilio, Sprout Social or comparable client systems is highly valuable.

Experience with automation/integration technologies such as n8n, Make, Zapier, MCP or comparable platforms/frameworks is also highly desirable.

The successful candidate is:

• Highly hands-on and technically curious.
• Comfortable building solutions independently.
• Extremely proficient in using AI to accelerate development.
• Able to understand customer-support operations.
• Pragmatic rather than technology-driven for technology's sake.
• Capable of rapidly turning an operational problem into a working prototype.
• Comfortable working across multiple clients, platforms and technology stacks.
• Able to communicate technical concepts clearly to non-technical stakeholders.
• Focused on measurable business results.

Key Success Metrics

The AI Implementation Manager will ultimately be measured by:

• Number of successful AI implementations deployed.
• Percentage of customer interactions automated or AI-assisted.
• Reduction in cost per interaction.
• Reduction in human handling time.
• Improvement or maintenance of CSAT and quality.
• AI resolution/containment rate.
• Implementation speed.