AI Expert for ATS CV Screening Module
Budget: $750 – $1,500 USD
AI/LLM Engineer – Production AI Screening Engine for ATS
About Us
We are building a next-generation Applicant Tracking System (ATS) that leverages AI to automate candidate screening, evaluation, and ranking.
Our ATS platform, backend, frontend, and databases are already production-ready.
We are not looking for someone to build an ATS.
We are looking for an experienced AI/LLM Engineer to build a production-grade AI Screening Engine that integrates with our existing ATS through REST APIs.
The goal is to achieve high-quality candidate evaluations while keeping AI costs as low as possible.
Project Overview
The selected engineer will design and implement an AI service responsible for evaluating candidates using one or multiple Large Language Models.
The service will receive candidate information from our ATS, intelligently process it, and return structured evaluation results in JSON format.
We expect the solution to be reliable, scalable, deterministic, and optimized for production use.
Existing System
We already have:
Production ATS
Backend APIs
Candidate workflows
CV parsing
Candidate database
Existing AI prompts
Production infrastructure
The AI service will simply consume our data and return structured evaluation results.
Responsibilities
Design and improve production-grade prompts.
Redesign our current prompt architecture if necessary.
Build a Python REST API (FastAPI preferred).
Integrate multiple LLM providers (OpenAI, Gemini, DeepSeek, etc.).
Recommend the most appropriate model for each task.
Optimize prompts for:
accuracy
consistency
reasoning quality
structured outputs
low hallucination rate
predictable behavior
Design robust JSON output schemas.
Implement validation and error handling.
Compare different AI models based on:
quality
latency
token usage
operational cost
Recommend a multi-model strategy that balances quality and cost.
Help integrate the AI service into our ATS.
Technical Requirements
Required:
Strong Prompt Engineering experience
Production experience with LLMs
Python
FastAPI
REST APIs
Pydantic
JSON Schema
OpenAI API
Google Gemini API
DeepSeek API
Git
We are not looking for simple prompt writing.
We are looking for someone who understands how to build reliable AI systems, including:
Prompt architecture
Model orchestration
Structured outputs
Validation
Cost optimization
Model benchmarking
Reliability
Production deployment
Deliverables
The selected engineer will deliver:
Production-ready prompt templates
AI evaluation workflow
Python REST API
Structured JSON schemas
Multi-model integration
Model selection strategy
Prompt versioning
Validation pipeline
Documentation
Integration guide
Cost Optimization
A major objective of this project is to achieve the best possible evaluation quality while minimizing AI costs.
The proposed solution should intelligently balance:
evaluation accuracy
reasoning capability
token consumption
API cost
latency
scalability
We are open to hybrid approaches that use different models for different stages of the evaluation pipeline.
About Us
We are building a next-generation Applicant Tracking System (ATS) that leverages AI to automate candidate screening, evaluation, and ranking.
Our ATS platform, backend, frontend, and databases are already production-ready.
We are not looking for someone to build an ATS.
We are looking for an experienced AI/LLM Engineer to build a production-grade AI Screening Engine that integrates with our existing ATS through REST APIs.
The goal is to achieve high-quality candidate evaluations while keeping AI costs as low as possible.
Project Overview
The selected engineer will design and implement an AI service responsible for evaluating candidates using one or multiple Large Language Models.
The service will receive candidate information from our ATS, intelligently process it, and return structured evaluation results in JSON format.
We expect the solution to be reliable, scalable, deterministic, and optimized for production use.
Existing System
We already have:
Production ATS
Backend APIs
Candidate workflows
CV parsing
Candidate database
Existing AI prompts
Production infrastructure
The AI service will simply consume our data and return structured evaluation results.
Responsibilities
Design and improve production-grade prompts.
Redesign our current prompt architecture if necessary.
Build a Python REST API (FastAPI preferred).
Integrate multiple LLM providers (OpenAI, Gemini, DeepSeek, etc.).
Recommend the most appropriate model for each task.
Optimize prompts for:
accuracy
consistency
reasoning quality
structured outputs
low hallucination rate
predictable behavior
Design robust JSON output schemas.
Implement validation and error handling.
Compare different AI models based on:
quality
latency
token usage
operational cost
Recommend a multi-model strategy that balances quality and cost.
Help integrate the AI service into our ATS.
Technical Requirements
Required:
Strong Prompt Engineering experience
Production experience with LLMs
Python
FastAPI
REST APIs
Pydantic
JSON Schema
OpenAI API
Google Gemini API
DeepSeek API
Git
We are not looking for simple prompt writing.
We are looking for someone who understands how to build reliable AI systems, including:
Prompt architecture
Model orchestration
Structured outputs
Validation
Cost optimization
Model benchmarking
Reliability
Production deployment
Deliverables
The selected engineer will deliver:
Production-ready prompt templates
AI evaluation workflow
Python REST API
Structured JSON schemas
Multi-model integration
Model selection strategy
Prompt versioning
Validation pipeline
Documentation
Integration guide
Cost Optimization
A major objective of this project is to achieve the best possible evaluation quality while minimizing AI costs.
The proposed solution should intelligently balance:
evaluation accuracy
reasoning capability
token consumption
API cost
latency
scalability
We are open to hybrid approaches that use different models for different stages of the evaluation pipeline.