AI IVR Chatbot
REMOTE
Job Description: Agentic AI Developer (Voice & Chat Automation)
Role Overview
We are seeking an experienced Agentic AI Developer to design, build, and optimize Voice and Chat Agentic Agents that handle customer interactions before they reach live agents. This role focuses on creating intelligent, autonomous agents that resolve customer needs through natural voice and chat experiences, leveraging reasoning, context, and backend integrations rather than static scripts or decision trees.
The ideal candidate brings a strong technical foundation in LLMs, agent orchestration, and distributed systems, along with experience evolving traditional IVR or chatbot solutions into modern, agentic AI systems.
Key Responsibilities
Agentic AI Development
Design and implement Voice and Chat Agentic Agents capable of multi-step reasoning, task execution, and dynamic decision-making
Develop agent architectures leveraging planning, tool use, memory management, and orchestration frameworks
Build systems that support autonomous resolution of customer intents end-to-end
Voice & Chat Experience Engineering
Engineer AI-driven voice and chat interaction layers that replace or augment traditional IVR and scripted bots
Implement NLU/NLP capabilities using LLMs, embeddings, and prompt orchestration
Design flexible, context-aware interactions that adapt dynamically to user input
Backend Integration & Action Execution
Build and maintain integrations with enterprise systems using REST/GraphQL APIs, event-driven architectures, and microservices
Enable agents to securely perform actions such as authentication, data retrieval, updates, and transactions
Implement middleware layers to manage agent-to-system communication, retries, and error handling
Architecture & Scalability
Design scalable, production-grade systems supporting high concurrency, low latency, and real-time interactions
Implement retrieval-augmented generation (RAG) using vector databases and knowledge stores
Optimize system performance, caching strategies, and inference efficiency
Evaluation, Observability & Optimization
Implement telemetry, logging, and tracing for agent interactions
Define and monitor metrics such as task completion, containment, latency, and failure modes
Build evaluation pipelines for testing prompts, workflows, and agent behaviors at scale
Safety & Control
Implement guardrails, policy controls, and validation layers to ensure safe and compliant AI behavior
Design fallback and escalation mechanisms when confidence or capability thresholds are not met