AWS AgentCore Python phone Voice AI Agent
Budget / SalaryC$10–30
TypeFreelance project
LocationRemote
Posted1 hour ago
Job Title: AWS AgentCore & Python Developer (Educational Project: phone Voice AI Agent Builder + Mentorship)
Job Type: Fixed-Price
Very good natural voices
caller phone number ID identification
email interface for admin to provided gmail address : add or remove files for skills; send by email logs of conversations , block phone numbers for incoming call
for LLM use low cost models from openai or gemini or make suggestion
No Terraform or similar techniques, only simple browser aws platform development
I use for local windows
Project Overview
I am looking for an experienced AWS and Python developer to build a scalable Voice AI Agent architecture using AWS Bedrock AgentCore. This is fundamentally an educational project: your goal is to build a fully working example system, document it exhaustively, and teach me how to reproduce, modify, and deploy it completely independently from scratch.
You may use existing open-source GitHub projects as a foundation, provided the final deliverable meets all my requirements and is fully documented.
System Architecture & Core Examples
We need a multi-tenant voice agent system acting as customer service for different "stores."
The Use-Case Examples:
Think of these as sales agents for different stores, where each store has many unique products:
Store 1: Food Catering
Store 2: Cleaning Services
Store 3: Refrigerator Repair
Expected Interaction Flow:
At the beginning of the call, the agent asks the user what they want to talk about (essentially asking which file to load).
Example A (Store 3): A user calls the dedicated phone number for Store 3 and says, "I have Refrigerator abc123." The agent must dynamically retrieve the specific .txt skill file for "Refrigerator abc123" and use this skill for the remainder of the session.
Example B (Store 2): A user calls the dedicated phone number for Store 2 and says, "I need to clean a sofa." The agent chooses the specific .txt skill file on how to clean a sofa and uses it for the session.
Technical Features & Requirements
Voice Integration: Phone call voice chat using a Canadian phone number (+1). One phone number serves exactly one agent/store.
Agent Registry & Scaling: An admin registry that can easily create anywhere from 0 to 100+ agents as needed.
Strict Data Isolation: Each agent has its own dedicated group of .txt knowledge files. All phone and skill files are strictly separate. The .txt skills for Store 1 must never be accessed or used by the agent for Store 3.
Session Memory: The agent must retain context during the call (e.g., if the caller says "My name is Peter," the agent calls him Peter for the whole session). There must be no memory between different sessions.
Logging: Detailed logs must be generated and saved for every individual call/session.
Technical Stack
Language: Python
AI Framework: AWS Bedrock AgentCore
Telephony: AWS services compatible with AgentCore Amazon Connect
Infrastructure: AWS
Strict Development Constraints
Your Own Environment: You must develop, record, and test this entirely in your own authorized AWS account. I will not provide my AWS account or credentials.
Zero Dependencies: My later deployment must not depend on your accounts, resources, or API keys.
Deliverables (Required for Payment)
This fixed-price project covers the application, deployment, security, tests, source code, reporting, manuals, videos, and a handover session.
1. Editable Word (.docx) Manual:
Must start from a totally clean environment.
Every actionable setup/configuration step needs a real, readable screenshot.
Numbered instructions with copyable commands/settings.
Explain the purpose of the step, the expected result, and how to verify it.
Clearly distinguish between Windows and Server commands and explain any placeholders (e.g., [YOUR_BUCKET_NAME]).
Must cover: Prerequisites, code structure, AgentCore setup, memory/RAG routing, telephony settings, database/logs, AWS IAM/security, tests, monitoring, recovery, updates, cost breakdown, and complete environment cleanup.
Include exact software/library versions and architecture details. Do not include real secrets/passwords in the doc. Include a section on common errors and fixes.
2. Narrated MP4 Videos:
Follow the exact section/step numbers of the .docx manual.
Include a timestamp index.
Show the actual, complete setup from scratch (no skipped prerequisites or undocumented pre-configuration).
Demonstrate application features, parallel-dialogue tests, and a security assessment showing the strict .txt file isolation.
Note: Videos, screenshots, and code must match perfectly.
3. Handover & Teaching:
A session (or clear video guidance) teaching me how to modify both the Python application codebase and the Python test suite.
Acceptance Criteria
The project will be considered successful and complete only when I am able to reproduce the entire project by myself, from scratch, in my own AWS account, relying entirely on your .docx manual and videos without needing to ask you for missing steps.
Job Type: Fixed-Price
Very good natural voices
caller phone number ID identification
email interface for admin to provided gmail address : add or remove files for skills; send by email logs of conversations , block phone numbers for incoming call
for LLM use low cost models from openai or gemini or make suggestion
No Terraform or similar techniques, only simple browser aws platform development
I use for local windows
Project Overview
I am looking for an experienced AWS and Python developer to build a scalable Voice AI Agent architecture using AWS Bedrock AgentCore. This is fundamentally an educational project: your goal is to build a fully working example system, document it exhaustively, and teach me how to reproduce, modify, and deploy it completely independently from scratch.
You may use existing open-source GitHub projects as a foundation, provided the final deliverable meets all my requirements and is fully documented.
System Architecture & Core Examples
We need a multi-tenant voice agent system acting as customer service for different "stores."
The Use-Case Examples:
Think of these as sales agents for different stores, where each store has many unique products:
Store 1: Food Catering
Store 2: Cleaning Services
Store 3: Refrigerator Repair
Expected Interaction Flow:
At the beginning of the call, the agent asks the user what they want to talk about (essentially asking which file to load).
Example A (Store 3): A user calls the dedicated phone number for Store 3 and says, "I have Refrigerator abc123." The agent must dynamically retrieve the specific .txt skill file for "Refrigerator abc123" and use this skill for the remainder of the session.
Example B (Store 2): A user calls the dedicated phone number for Store 2 and says, "I need to clean a sofa." The agent chooses the specific .txt skill file on how to clean a sofa and uses it for the session.
Technical Features & Requirements
Voice Integration: Phone call voice chat using a Canadian phone number (+1). One phone number serves exactly one agent/store.
Agent Registry & Scaling: An admin registry that can easily create anywhere from 0 to 100+ agents as needed.
Strict Data Isolation: Each agent has its own dedicated group of .txt knowledge files. All phone and skill files are strictly separate. The .txt skills for Store 1 must never be accessed or used by the agent for Store 3.
Session Memory: The agent must retain context during the call (e.g., if the caller says "My name is Peter," the agent calls him Peter for the whole session). There must be no memory between different sessions.
Logging: Detailed logs must be generated and saved for every individual call/session.
Technical Stack
Language: Python
AI Framework: AWS Bedrock AgentCore
Telephony: AWS services compatible with AgentCore Amazon Connect
Infrastructure: AWS
Strict Development Constraints
Your Own Environment: You must develop, record, and test this entirely in your own authorized AWS account. I will not provide my AWS account or credentials.
Zero Dependencies: My later deployment must not depend on your accounts, resources, or API keys.
Deliverables (Required for Payment)
This fixed-price project covers the application, deployment, security, tests, source code, reporting, manuals, videos, and a handover session.
1. Editable Word (.docx) Manual:
Must start from a totally clean environment.
Every actionable setup/configuration step needs a real, readable screenshot.
Numbered instructions with copyable commands/settings.
Explain the purpose of the step, the expected result, and how to verify it.
Clearly distinguish between Windows and Server commands and explain any placeholders (e.g., [YOUR_BUCKET_NAME]).
Must cover: Prerequisites, code structure, AgentCore setup, memory/RAG routing, telephony settings, database/logs, AWS IAM/security, tests, monitoring, recovery, updates, cost breakdown, and complete environment cleanup.
Include exact software/library versions and architecture details. Do not include real secrets/passwords in the doc. Include a section on common errors and fixes.
2. Narrated MP4 Videos:
Follow the exact section/step numbers of the .docx manual.
Include a timestamp index.
Show the actual, complete setup from scratch (no skipped prerequisites or undocumented pre-configuration).
Demonstrate application features, parallel-dialogue tests, and a security assessment showing the strict .txt file isolation.
Note: Videos, screenshots, and code must match perfectly.
3. Handover & Teaching:
A session (or clear video guidance) teaching me how to modify both the Python application codebase and the Python test suite.
Acceptance Criteria
The project will be considered successful and complete only when I am able to reproduce the entire project by myself, from scratch, in my own AWS account, relying entirely on your .docx manual and videos without needing to ask you for missing steps.
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