AI Developer – For Chennai location

CogNet HRO; Chennai District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

CogNet HR Solutions seeks an experienced AI Developer to design and build an AI-powered document processing and workflow automation platform. You will handle ingestion, classification, extraction, validation, reasoning, and enterprise integration across diverse documents.

The role emphasizes hands-on engineering—moving from prototype to secure production with scalable AI services on AWS, containerized via Docker, and integrated with CogNet applications.

Qualifications

• Bachelor's or Master's degree in CS/AI/Data Science or related field.
• 5+ years of software development experience.
• Strong Python and LLM API experience.
• Experience building REST APIs and backend services.
• Experience processing PDFs and business documents.

Responsibilities

• Design and develop AI applications using LLMs.
• Build AI agents for document classification, extraction, validation, reasoning, and workflow execution.
• Develop structured extraction pipelines converting documents to JSON and database records.
• Implement prompt engineering, structured outputs, and agent workflows.
• Evaluate models for accuracy, latency, and cost.
• Build human-in-the-loop review routing and guardrails.

Skills

Python programming

LLMs

REST APIs

FastAPI

AI model integration

Education

Bachelor's or Master's in CS/AI/Data Science

Tools

LangChain

LangGraph

Semantic Kernel

LlamaIndex

C# / ASP.NET Core

PyMuPDF

OCR technologies

AWS Textract

Azure Document Intelligence

OpenCV

SQL Server / PostgreSQL

Redis

Job description

Cognet HRO is a leading Business Process Outsourcing Services Company providing full range of HR and F&A services to US based clients , With over 17 years of rich experience in Payroll Tax, Benefits S & HR Administration, Finance & Accounting , Sales Support . CogNet has been serving the PEO, ASO, HRO and HR Technology spaces since our inception. We help organizations extend their capabilities through simplified implementation, productivity performance measured to the minute, easy collaboration, and transparent pricing built around real time utilization.Our extensive expertise, data library, and workflow development tools accelerates the client implementation process.We have developed a deep expertise of process and technology in our Services , which allows us to rapidly deliver value to our clients. Cognet has been delivering outsourced solutions to the clients around the Globe.

Job Description

About the Role

CogNet HR Solutions is seeking an AI Developer to design and build our AI-powered intelligent document processing and workflow automation platform which will process complex business documents such as PDFs, scanned documents, spreadsheets, forms, invoices, payroll reports, insurance documents, loss runs, applications, contracts, and other structured and unstructured data.

The AI Developer will work across the complete AI pipeline from document ingestion and classification through extraction, validation, reasoning, workflow automation, human review, and enterprise integration. This is a hands-on engineering role for someone who can take AI solutions from prototype to secure, reliable production.

Preferred Technology Stack

AI Frameworks: LangChain, LangGraph, Semantic Kernel, LlamaIndex, and agent/tool-calling frameworks.

Programming: Strong Python proficiency; FastAPI, Pydantic, REST APIs, JSON and JSON Schema. Experience integrating AI services with C# / ASP.NET Core is highly desirable.

Document Processing: PyMuPDF/PDF libraries, OCR technologies, AWS Textract, Azure Document Intelligence, Document AI, OpenCV, and Excel processing.

Data: SQL Server/PostgreSQL, vector databases, embeddings, semantic search, Redis or equivalent caching.

Requirements

Key Responsibilities

AI & LLM Development

• Design and develop AI applications using Large Language Models (LLMs).
• Build AI agents for document classification, extraction, validation, reasoning, and workflow execution.
• Develop structured extraction pipelines that convert unstructured documents into validated JSON and database records.
• Implement prompt engineering, structured outputs, function/tool calling, and agent workflows.
• Evaluate models based on accuracy, latency, security, and cost.
• Build confidence scoring and human-review routing for exceptions.
• Implement guardrails and validation to minimize hallucinations and incorrect extraction.
• Document type detection and classification.
• OCR and vision-based document understanding.
• Table, form, key-value, and entity extraction.
• Multi-page processing, document splitting, and routing.
• Cross-document comparison and reconciliation.
• Confidence scoring, exception handling, and human-in-the-loop review.
• Structured JSON output generation.

Agentic Workflow Development

• Develop agents that identify document types and select appropriate extraction schemas.
• Extract and validate information against business rules.
• Compare multiple documents and identify missing or inconsistent information.
• Call APIs and enterprise systems and route documents to appropriate workflows.
• Generate summaries and recommendations and elevate exceptions for human review.
• Design reliable orchestration with retries, logging, fallbacks, and error handling.

Application & API Development

• Develop backend AI services and REST APIs.
• Integrate AI services with CogNet applications and third-party enterprise platforms.
• Build asynchronous/background processing for large document workloads.
• Design scalable queues and batch-processing capabilities.
• Implement secure API authentication and authorization.
• Deploy and operate AI applications on AWS.
• Build containerized applications using Docker.
• Implement scalable AI inference and document-processing architectures.
• Monitor model performance, latency, token consumption, failures, and cost.
• Implement production logging, monitoring, retries, and exception handling.
• Retrieval-Augmented Generation (RAG), embeddings, vector search, and hybrid search.
• Chunking strategies, metadata filtering, document retrieval, and citation/source grounding.
• Enterprise document search or knowledge-management experience is an advantage.

AI Quality & Evaluation

• Create AI evaluation datasets and measure extraction accuracy.
• Compare model performance and track false positives/false negatives.
• Test prompts and extraction schemas and implement AI regression testing.
• Monitor production AI quality and optimize model selection for accuracy, speed, and cost.

Security & Data Privacy

• Encryption in transit and at rest, role-based access control, and secure API design.
• Secrets management, audit logging, data isolation, and PII handling.
• Secure LLM integration and protection against prompt injection and malicious documents.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related discipline.
• 5+ years of software development experience.
• Strong Python programming skills and practical experience building applications using LLM APIs.
• Experience developing REST APIs and backend services.
• Experience processing PDFs and other business documents.
• Strong understanding of structured and unstructured data and SQL databases.
• Familiarity with cloud platforms, preferably AWS.
• Strong analytical and problem-solving skills.

Preferred Qualifications

• Intelligent Document Processing (IDP) and AI agents/agentic workflows.
• Insurance or Workers' Compensation documents, payroll/HR data, or financial documents.
• Invoice processing, document reconciliation, and enterprise workflow automation.
• RAG applications, AWS Bedrock, AWS Textract, OpenAI APIs, or high-volume document-processing systems.

What You Will Build

• Insurance submission processing and Workers' Compensation loss-run analysis.
• Payroll and HR document extraction.
• Benefits reconciliation, invoice processing, and bank reconciliation.
• Contract/form extraction, document comparison, and AI-powered enterprise document search.

What Success Looks Like

• Build production-ready document AI pipelines supporting multiple document types and configurable schemas.
• Achieve measurable extraction accuracy and establish automated evaluation and monitoring.
• Develop reusable AI agents and services and integrate Drive with CogNet applications.
• Implement human-in-the-loop exception workflows.
• Improve document-processing speed and automation rates while reducing inference cost.

Ideal Candidate

We are looking for a builder rather than someone focused only on AI research. The ideal candidate can take a business problem such as: Read these 200-page insurance documents, identify the document types, extract the required information, validate the information, identify exceptions, and return structured data through an API, and design and implement the complete production solution.

The candidate should be comfortable experimenting with new AI models while maintaining the engineering discipline required for secure, reliable enterprise software.