Voice AI Agent

Role - Voice AI Agent

Duration - 6-month contract, possibly extended

Overlap until 9:30 PM IST

Location - Remote

Skills -

AWS Ecosystem for Voice AI Amazon Connect, Lex, Bedrock, Python, Eleven Labs, good to have Sales Force integration. This is for Voice AI Agents

Own a production voice AI agent end to end: keep it stable through incident and transcript-driven tuning, redesign it from scripted verification plus Q&A into a fully agentic, lower-latency conversation, and ship every release through automated quality gates, PII/PHI safeguards, governed Azure DevOps pipelines, and active monitoring of performance and cost.

Must-have skills (please screen for these first)

· Has built and launched at least one LLM app used by real customers or employees. Demos and proofs of concept dont count. Strong in prompt engineering, tool calling, structured output, and RAG.

• · Hands-on with Amazon Connect and Amazon Lex V2: contact flows, Lambda integrations, intents and slots
• · Amazon Bedrock or a comparable managed LLM service: knowledge bases, agents, guardrails
• · Has built evaluation suites for LLM output (golden answers, LLM-as-judge, or similar)
• · Strong Python, plus AWS core services: Lambda, IAM, DynamoDB, S3, CloudWatch, KMS
• · Has worked with sensitive data in a regulated industry (insurance, healthcare, or financial services) under formal change control

Strongly preferred

• · Voice AI experience, such as ElevenLabs or similar text-to-speech and speech-to-text tools, and reducing latency in real-time voice apps
• · Agentic orchestration: Bedrock Agents or Flows, Step Functions, EventBridge, SQS/SNS
• · Terraform, or CDK/CloudFormation
• · Azure DevOps, including YAML pipelines with AWS service connections
• · Java (Spring Boot) or TypeScript/Node

Nice to have

• · Contact Lens, Amazon Q in Connect, Kinesis
• · Vector stores such as OpenSearch Serverless or pgvector
• · Datadog
• · Insurance or benefits domain background

Not a fit

• · Data scientists or ML researchers who focus on training or fine-tuning models
• · People who have only built chatbots on no-code platforms
• · People whose AI experience is limited to courses, hackathons, or personal projects