GenAI CCAS Application Developer

Location - REMOTE - with quarterly travel to Gaithersburg, MD

Role Type - 6-month contract with possibility of extension

About Our Client

Our client is a mission-driven technology organization that delivers secure, innovative, and scalable solutions to support enterprise and government environments. They specialize in cloud-based services, intelligent automation, and data-driven systems that enhance operational efficiency and customer experience. The company values collaboration, technical excellence, and continuous learning, providing a dynamic environment where employees can grow their skills while making meaningful contributions.

Job Description

We are seeking an early career GenAI Application Developer to build and enhance AI-powered applications for an AWS Contact Center As a Service (CCAS) platform leveraging Amazon Connect, Lex V2, Amazon Q in Connect, Contact Lens, Bedrock, Open Search, and Lambda (Python).

This is a software development role focused on conversation flow design, Amazon Connect integrations, AI service orchestration, generative AI safety, and the creation of supporting APIs and data pipelines to improve customer experience through intelligent automation.

You will work with senior developers, UI/UX designers, and cloud engineers to implement features that deflect customer contacts with voice/chat bots, optimize routing and flows, provide agents with real-time AI assistance, and ensure privacy/compliance controls. This is an exciting opportunity to grow your development skills while delivering secure, reliable, and innovative solutions in a mission environment.

Duties and Responsibilities

• Build, update, and maintain Amazon Connect contact flows, routing profiles, and queues to support both voice and chat channels.
• Integrate Lex V2 bots into Amazon Connect flows for call deflection, self-service transactions, and escalation.
• Enable and configure Contact Lens for real-time and post-contact analytics, transcription, summarization, sentiment analysis, and redaction.
• Configure Amazon Q in Connect domains, knowledge sources, guided workflows, and step-by-step agent assist experiences.
• Implement S3-based call and chat transcript storage with encryption, lifecycle policies, and retention compliance.
• Write AWS Lambda (Python) functions to orchestrate Bedrock LLM calls, embeddings workflows, and model invocation logging.
• Implement Open Search indexing, vector/keyword queries, and knowledge synchronization triggers.
• Build retrieval-augmented generation (RAG) pipelines to enhance Amazon Connect agent assist and self-service knowledge.
• Apply structured logging, unit/integration tests, error handling, and performance/cost safeguards.
• Implement AI guardrails, prompt templates, and output evaluation for safety and accuracy.
• Enforce PII minimization and redaction policies in Amazon Connect conversation logs.
• Participate in threat modeling and support remediation of findings for contact center integrations.
• Support Cloud Front + WAF configurations for secure web chat entry points.
• Build APIs and event hooks to pass conversation context between web chat, Amazon Connect, and AI services.
• Contribute to Git-based CI/CD pipelines, code reviews, and documentation.
• Maintain runbooks, architecture diagrams, and SOPs for contact flows, bot integrations, and AI workflows.
• Create and monitor Cloud Watch dashboards/alarms for call deflection rate, average handle time (AHT), contact resolution, and AI usage metrics.

Required Experience/Skills

• Bachelor’s degree in Computer Science, Engineering, or related field and 2–4 years of relevant experience; or a Master’s with.
• 2+ years of software development with Python, including building and troubleshooting AWS Lambda functions.
• Hands-on experience with Amazon Connect setup and configuration, including contact flows, routing profiles, queues, and channel integration (voice, chat).
• Experience integrating Lex V2 bots with Amazon Connect flows.
• Familiarity with conversational AI design, including slot elicitation, error recovery, and safe fallback patterns.
• Infrastructure as Code experience (Cloud Formation) and Git-based CI/CD workflows.
• Foundational security knowledge: least privilege…