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Ai Ocr Jobs (NOW HIRING)

Insight Global

AI Engineer

Atlanta, GA · On-site +1

... OCR pipelines as the platform serves new client engagements. • Implement per-tenant isolation ... AI -- PDF parsing, table extraction, or OCR pipelines. • Has contributed to or built an ...

Pereview Software, LLC.

AI Engineer

Dallas, TX · On-site

$90 - $120/hr

Pereview Software is seeking an AI Engineer to join our growing Product and Engineering team. This ... Build and optimize document ingestion, OCR, and data extraction workflows for financial and real ...

Luma Financial Technologies, LLC

AI/ML Engineer

Miami, FL · On-site

$100 - $130/hr

About the role We are seeking a talented AI/ML Engineer with a minimum of 3 years of hands‑on ... Develop and enhance OCR capabilities and integrate these with vector databases. * Utilize Retrieval ...

CYNET SYSTEMS

AI Developer

Charlotte, NC · On-site

$55 - $60/hr

Hands‑on experience with Document AI / IDP platforms (OCR, PDF parsing) based extraction. * Experience with confidence scoring, rule-based validation, semantic or fuzzy comparison, and API ...

Simbe Robotics, Inc.

$160 - $200/hr

Simbe is building the AI powered operating system for physical retail. Our autonomous robots and ... This role will work across dense product detection, price tag and promo tag detection, OCR, barcode ...

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Ai Ocr information

What is an AI OCR specialist?

An AI OCR (Optical Character Recognition) specialist is a professional who develops, implements, or manages systems that use artificial intelligence to recognize and extract text from images or scanned documents. These specialists work with machine learning models and OCR software to improve the accuracy and efficiency of digitizing printed or handwritten materials. Their work is essential for automating data entry, document processing, and enabling searchable digital archives in various industries.

What are the key skills and qualifications needed to thrive as an AI OCR specialist?

To thrive as an AI OCR Specialist, you need a solid background in computer science, machine learning, and image processing, often supported by a relevant degree or certifications. Familiarity with programming languages (such as Python), OCR libraries (like Tesseract or Google Vision), and deployment platforms is essential. Strong analytical skills, attention to detail, and effective communication help you interpret results and collaborate across teams. These skills ensure accurate text extraction, efficient workflow integration, and successful AI-driven document processing solutions.

What are some typical challenges faced by professionals working in AI OCR roles, and how can they be addressed?

Professionals in AI OCR (Optical Character Recognition) roles often encounter challenges such as handling poor-quality or complex documents, dealing with handwritten or non-standard fonts, and ensuring high accuracy across multiple languages. Overcoming these challenges typically involves collaborating closely with data scientists, software engineers, and product teams to improve data preprocessing, model training, and validation processes. Keeping up with the latest advancements in machine learning and OCR technologies also helps in implementing more robust solutions. Regularly reviewing errors and iterating on AI models are essential practices to maintain and enhance the system's performance.

What is the difference between Ai Ocr vs Data Entry Specialist?

AspectAi OcrData Entry Specialist
Required CredentialsBasic technical knowledge, sometimes certifications in AI or OCR toolsHigh school diploma or equivalent, data management skills
Work EnvironmentMostly digital, software-based tasks, often remoteOffice or remote, handling physical or digital data
Employer & Industry UsageTech companies, document processing firms, automation industriesAdministrative, finance, healthcare, and various sectors
Search & Comparison IntentUnderstanding automation tools, OCR technologyManual data input, accuracy, and efficiency

Ai Ocr involves using artificial intelligence to automatically recognize and extract data from images or scanned documents, streamlining data processing. Data Entry Specialists manually input data into systems, focusing on accuracy and speed. While Ai Ocr automates data extraction, Data Entry Specialists handle tasks requiring human oversight and validation.

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Ai Ocr jobs near you
Infographic showing various Ai Ocr job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 21% Part Time, and 3% Contract. Highlights an 64% Physical, 4% Hybrid, and 32% Remote job distribution.

AI Engineer

Insight Global

Atlanta, GA • On-site, Remote

Full-time

Re-posted 22 days ago

Job description

Overview

You will work across the AI layer of the platform — contributing to retrieval pipelines, agent workflows, and model evaluation. The work is hands-on and empirical: you run experiments, measure results, and iterate. You will work closely with the Senior AI Engineer and broader engineering team, taking increasing ownership as you develop depth across the platform's AI systems.


Responsibilities

• Contribute to the hybrid retrieval pipeline — implementing and tuning retrieval components, running experiments to improve quality, and validating results on real evaluations.
• Build and maintain components of the agent orchestration layer — tool integrations, prompt management, and supporting human-in-the-loop workflows.
• Instrument AI and agent systems for observability — tracing model and tool calls, capturing token and latency telemetry, and supporting failure analysis.
• Build and maintain evaluation datasets and test suites across retrieval and agent workflows, contributing to CI-level quality gates.
• Support document AI capabilities — working with parsing, extraction, and OCR pipelines as the platform serves new client engagements.
• Implement per-tenant isolation checks across retrieval and agent layers, helping ensure no cross-tenant data leakage occurs.
• Contribute to model behaviour evaluation — running prompt injection and adversarial tests as part of ongoing eval work.


Qualifications

Required qualifications
• 3+ years of software engineering experience, with at least 1 year building LLM-powered systems — RAG pipelines, agent workflows, or fine-tuning — in a production or nearproduction setting.
• Practical experience in at least one of: retrieval-augmented generation (RAG) and reranking; agent orchestration with LangGraph or comparable; or LLM fine-tuning.
• Proficient in Python and comfortable working with async code, data pipelines, and REST APIs.
• Exposure to evaluation methodology for LLM systems — has contributed to or authored an eval dataset or test suite.
• Familiarity with agent or LLM observability tooling — tracing, logging, or monitoring of model and tool calls.
• Working knowledge of modern LLM and information-retrieval concepts; can discuss trade-offs between approaches with evidence.

Preferred qualifications
• Experience with knowledge graphs or property-graph query languages.
• Exposure to document AI — PDF parsing, table extraction, or OCR pipelines.
• Has contributed to or built an evaluation dataset and labeling workflow.
• Familiarity with prompt-injection risks and mitigation strategies in agent and retrieval pipelines.
• Open-source contributions to LangGraph, sentence-transformers, or comparable projects.

Qualifications:

Required qualifications
• 3+ years of software engineering experience, with at least 1 year building LLM-powered systems — RAG pipelines, agent workflows, or fine-tuning — in a production or nearproduction setting.
• Practical experience in at least one of: retrieval-augmented generation (RAG) and reranking; agent orchestration with LangGraph or comparable; or LLM fine-tuning.
• Proficient in Python and comfortable working with async code, data pipelines, and REST APIs.
• Exposure to evaluation methodology for LLM systems — has contributed to or authored an eval dataset or test suite.
• Familiarity with agent or LLM observability tooling — tracing, logging, or monitoring of model and tool calls.
• Working knowledge of modern LLM and information-retrieval concepts; can discuss trade-offs between approaches with evidence.

Preferred qualifications
• Experience with knowledge graphs or property-graph query languages.
• Exposure to document AI — PDF parsing, table extraction, or OCR pipelines.
• Has contributed to or built an evaluation dataset and labeling workflow.
• Familiarity with prompt-injection risks and mitigation strategies in agent and retrieval pipelines.
• Open-source contributions to LangGraph, sentence-transformers, or comparable projects.

Education:UNAVAILABLEEmployment Type: FULL_TIME

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