Amazon Connect

Role & responsibilities

- Design and implement Amazon Connect contact-center solutions, including contact flows, queues, routing profiles, security profiles, agent workspace integrations, and telephony configurations.
- Build generative AI use cases using Amazon Bedrock, including prompt design, foundation-model integration, guardrails, grounding, evaluation, monitoring, and responsible-AI controls.
- Configure and extend Amazon Q in Connect / Amazon Connect AI agents to provide contextual knowledge, recommended responses, next-best actions, and automated assistance during and after customer interactions.
- Implement Amazon Connect Contact Lens capabilities, including transcription, sentiment and conversational analytics, quality insights, and post-contact call summarization.
- Create and maintain knowledge bases and content-ingestion pipelines so AI assistants and agents return accurate, traceable, and current answers.
- Develop step-by-step guides and agent workflows that guide users through business processes, troubleshooting, compliance steps, and customer-service actions.
- Design and implement Amazon Connect outbound campaigns, including campaign flows, customer lists, retry logic, answering-machine detection, quotas, scheduling, monitoring, and regulatory controls.
- Outbound campaigns campaign setup, dialing modes, campaign management, segmentation
- Amazon Connect outbound campaign flows designing and implementing customer journeys for outbound calls.
- Develop custom reports, dashboards, and data pipelines using Amazon Connect metrics, Contact Trace Records, Contact Lens data, Amazon S3, Amazon Athena, Amazon QuickSight, or approved enterprise reporting platforms.
- Build production-grade AWS Lambda functions and APIs for integrations, orchestration, validation, event processing, error handling, logging, and retry mechanisms.
- Design DynamoDB data models and event-driven workflows using Amazon EventBridge, including schedules, rules, event routing, idempotency, and failure recovery.
- Integrate Amazon Connect with CRM, case-management, identity, knowledge, reporting, and line-of-business systems using secure APIs and AWS services.
- Create technical designs, implementation guides, test evidence, runbooks, deployment plans, and knowledge-transfer materials participate in code reviews and production support.

Preferred candidate profile

- Must Have Skills: Proficiency in Amazon Connect and Bedrock. Strong hands-on experience with contact flows, queues, routing, agent workspace, tasks, security profiles, Contact Trace Records, APIs, CloudWatch logging, and environment promotion.
- Generative AI: Practical experience with Amazon Bedrock, prompt engineering, model selection, grounding/RAG, guardrails, evaluation, observability, security, privacy, and cost optimization.
- AI Agent Assistance: Experience implementing Amazon Q in Connect or current Amazon Connect AI agent capabilities, including assistant configuration, knowledge integration, recommendations, actions, and agent-workspace adoption.
- Contact Lens: Experience with transcription, conversational analytics, sentiment, quality management, redaction, and AI-generated post-contact summaries.
- Knowledge Management: Ability to organize, ingest, index, govern, and refresh enterprise knowledge for accurate AI-assisted responses.
- Outbound Campaigns: Experience configuring voice or digital campaigns, dedicated queues and flows, dialer behavior, answering-machine detection, quotas, retries, caller identity, compliance, and performance monitoring.
- Serverless Development: Strong Lambda development skills in Python, Node.js, or Java, with API integration, IAM, Secrets Manager, KMS, structured logging, automated testing, and CI/CD practices.
-Data and Events: Hands-on knowledge of DynamoDB partition-key design, indexes, streams, transactions, TTL, and EventBridge event buses, rules, schedules, retries, and dead-letter handling.
- Reporting: Ability to customize operational and business reporting using Connect metrics and AWS analytics services strong understanding of contact-center KPIs and data quality.
- Engineering Practices: Proficiency with Git, infrastructure as code such as AWS CloudFormation, CDK, or Terraform, automated deployment, unit/integration testing, troubleshooting, and Agile delivery.