Senior Python Engineer — LLM + Computer Vision Document Extraction

@ZohaibRoy

⭐⭐⭐⭐⭐ Enhance Python Pipeline for Accurate Data Extraction from PDFs ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you're looking for someone to improve your Python pipeline for data extraction from PDFs. Look no further; Zohaib is here to help you! My team has successfully completed over 50 similar projects focused on Python data extraction and computer vision. Let me explain how I will enhance your pipeline, improve accuracy, and ensure robustness within your budget. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in Python development and computer vision. My expertise includes working with OpenCV, LLMs, and structured data extraction. Additionally, I have a strong grip on enhancing pipeline efficiency and robust error handling. ➡️ Let's have a quick chat to discuss your project in detail, and I can show you examples of my previous work. I'm excited to explore how we can improve your pipeline together! ➡️ Skills & Experience: ✅ Python Programming ✅ OpenCV ✅ LLM Integration ✅ Data Extraction ✅ Computer Vision ✅ PDF Rendering ✅ Error Handling ✅ Performance Optimization ✅ Asynchronous Programming ✅ Observability Tools ✅ Structured Data Output ✅ Image Processing Waiting for your response! Best Regards, Zohaib

@neerajdhameliya

Hello, Your current pipeline struggles when noisy or low-quality documents disrupt the normal computer vision flow. Small pixel changes or poor PDF rendering can confuse OpenCV contour and line tracing, causing expensive LLM fallbacks. I can make the system more reliable by improving the image processing steps with adaptive thresholding and better line thinning techniques, helping measurements stay accurate even on complex documents. I will also reduce Anthropic and Gemini costs by optimizing image sizes and using prompt caching. Additionally, I'll implement async retry logic and integrate Langfuse with Sentry for automatic issue detection. Happy to jump on a quick call and share examples of similar accuracy improvements on messy inputs. Best, Niral

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@ShaikhAneesa

Hello, I have carefully reviewed the project requirements for the Senior Python Engineer position involving LLM and Computer Vision document extraction. I understand the need for improving detection accuracy, sharpening the CV measurement path, hardening the pipeline, reducing LLM cost and latency, and enhancing observability for regression detection. To handle your project, I will start with enhancing the detection algorithms using a combination of Vision LLMs like Anthropic Claude and Gemini, along with OpenCV and PyMuPDF for rendering and measurement. I will focus on optimizing the staged pipeline architecture in Python, implementing robust error handling mechanisms, and improving the overall accuracy and efficiency of the document extraction process. Deliverables include improved detection accuracy on complex documents and a more resilient and cost-effective pipeline for structured data extraction. Before signing-off my bid, I would like to ask a question, i.e., "Have you considered integrating machine learning models for further enhancing the accuracy of the extraction process?" Best Regards, Aneesa.

@Herculesz

Hi I can take ownership of your production Python PDF extraction pipeline and improve accuracy, robustness, latency, and LLM cost across the full staged workflow. The main technical challenge is that dense technical PDFs can fail at several layers—rendering, reference-key interpretation, spatial detection, contour tracing, scale detection, retries, and structured output—and I would solve this with stronger validation logic, keep-best strategies, better CV-first measurement paths, and provider-aware LLM fallbacks. I’m strong with Python async pipelines, OpenCV, PyMuPDF, contour/area measurement, skeleton-based linear tracing, image geometry, structured LLM outputs, and multi-provider vision workflows using Claude, Gemini, and Vertex AI. I would focus first on improving recall on noisy pages, reducing false negatives, tightening scale and measurement logic, and lowering fallback frequency through better preprocessing and confidence scoring. I can also harden partial-page failures, timeout handling, retry policies, idempotency, regression checks, and observability through Sentry, Axiom, Langfuse, and structured evaluation datasets. Similar work I have handled includes LLM-assisted document extraction, noisy PDF parsing, OCR/CV validation, spatial object detection, and production pipeline optimization where accuracy and cost both mattered. Thanks, Hercules

@skalogir

Hi, I understand you need ownership of a production Python PDF extraction pipeline using vision LLMs, PyMuPDF, OpenCV contour/area measurement, skeleton tracing, scale detection, retries, cost control, and observability across Axiom, Sentry, and Langfuse. I have worked on production LLM/CV pipelines for noisy technical documents, improving recall on dense pages, reducing fallback usage with stronger geometry logic, and stabilizing structured multi-provider outputs from Claude/Gemini-style vision workflows. I would start by building an evaluation set, instrumenting page/object-level metrics, tuning detection prompts and spatial validation, hardening async stages with idempotent retries/keep-best logic, and optimizing CV-first measurement to lower latency and LLM spend. Q1: Do you already have labeled ground-truth PDFs for accuracy and recall testing? Q2: Which failure hurts most today: missed detections, wrong measurements, fallback cost, or silent degradation? Q3: Is the priority short-term accuracy improvement or long-term pipeline ownership? Best regards, Stratos

@usmanbashir12100

Hi, This aligns closely with the kind of AI and data-processing systems I enjoy working on. I have experience building Python-based data pipelines, LLM integrations, computer vision workflows, and production systems focused on reliability, observability, and cost optimization. Your challenge sounds less like "extracting data" and more like improving precision, resilience, and operational efficiency at scale. I can help with: • Improving detection accuracy and recall on complex PDFs • Optimizing OpenCV contour, skeletonization, and measurement pipelines • Reducing Vision LLM fallback rates through stronger CV preprocessing • Hardening pipeline reliability (retries, idempotency, timeout handling, keep-best logic) • Lowering LLM costs and latency through routing, caching, and model selection • Expanding observability using Sentry, Langfuse, and structured metrics • Performance tuning for async/concurrent Python workloads I'd be interested in reviewing a few sample documents and understanding where accuracy or cost is currently breaking down most often. Best, Muhammad Usman Full Stack Developer | AI | AWS & Azure | Python | LLM & CV Systems

@pkundu25

I am a Senior Python Engineer specializing in optimizing multi-provider LLM and Computer Vision pipelines. I am ready to take full ownership of your staged document extraction system to maximize accuracy, resilience, and cost efficiency. How I Will Advance Your Pipeline Sharper CV & Fewer Fallbacks: I will refine your OpenCV contour/skeleton tracing and scale detection to handle messy PDFs, directly reducing expensive LLM fallback rates. Hardened Architecture: I will implement resilient async patterns for partial-page failures, timeouts, idempotency, and keep-best logic to eliminate silent degradation. Cost & Latency Reductions: I will optimize Claude/Gemini spatial detection and structured tool-use via strict context management to slash API spend. Data-Driven Observability: I will integrate automated metrics into Langfuse, Sentry, and Axiom to catch regressions in data, not by eye. Stack Match Core: Python (Async/Concurrent), PyMuPDF, OpenCV (geometry/contours). AI/Ops: Claude, Gemini, Langfuse. Past Success: I recently rebuilt a technical drawing pipeline. By upgrading OpenCV pre-processing filters and skeleton tracing, I slashed LLM fallbacks by 42% and API costs by 35% while boosting recall. Let's discuss how to optimize your system.

@corpmember29

HELLO, I HAVE CAREFULLY REVIEWED YOUR REQUIREMENTS AND UNDERSTAND THAT YOU ARE LOOKING FOR A SENIOR PYTHON ENGINEER TO TAKE OWNERSHIP OF AN EXISTING PRODUCTION DOCUMENT EXTRACTION PIPELINE, WITH A PRIMARY FOCUS ON IMPROVING ACCURACY, ROBUSTNESS, OBSERVABILITY, AND COST EFFICIENCY ACROSS BOTH LLM AND COMPUTER VISION WORKFLOWS. **** You can track the project’s progress using the tracker. I’m available to work 40 -45 hours per week **** I HAVE 10 YEARS OF EXPERIENCE IN PYTHON, COMPUTER VISION, AI/ML SYSTEMS, LLM INTEGRATIONS, DOCUMENT PROCESSING, AND PRODUCTION PIPELINE OPTIMIZATION, I HAVE WORKED ON SOLUTIONS INVOLVING OCR, OBJECT DETECTION, IMAGE GEOMETRY, CONTOUR ANALYSIS, STRUCTURED DATA EXTRACTION, AND MULTI-MODEL AI ORCHESTRATION. I CAN HELP WITH: • IMPROVING DETECTION ACCURACY & RECALL ON COMPLEX DOCUMENTS • OPTIMIZING OPENCV CONTOUR, AREA & SKELETON TRACING LOGIC • REDUCING LLM FALLBACK DEPENDENCY AND INFERENCE COSTS • HARDENING PIPELINES WITH RETRIES, TIMEOUTS & FAILURE RECOVERY • IMPLEMENTING IDPOTENT PROCESSING & KEEP-BEST STRATEGIES • ENHANCING OBSERVABILITY THROUGH LANGFUSE, SENTRY & CUSTOM METRICS • OPTIMIZING ASYNC PYTHON WORKFLOWS FOR PERFORMANCE & SCALABILITY • IMPROVING STRUCTURED OUTPUT RELIABILITY ACROSS MULTIPLE LLM PROVIDERS I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT, COMPLETE SOURCE CODE OWNERSHIP, TECHNICAL DOCUMENTATION, AND FULL ASSISTANCE THROUGH ANALYSIS, OPTIMIZATION, TESTING, AND PRODUCTION DEPLOYMENT. THANK YOU.

@mubeenm5026

As a Python expert with over a decade of experience, I’m excited about the opportunity to work with you on your complex document extraction project. I’ve managed and successfully completed numerous projects similar to yours, particularly in the field of AI automation and machine learning — areas that are vital to the accuracy and efficiency improvements you’re seeking. Combining my deep knowledge of Python, computer vision, and image geometry (especially OpenCV contour/skeleton work), I’m confident that my skillset is perfectly aligned with your project's requirements. At Web Crest, my dedicated team and I have developed numerous intelligent solutions that have consistently delivered on performance and quality. Our approach is always geared towards not just solving the immediate problem under examination but on building robust infrastructures that can keep scaling even as your business grows exponentially. Finally, one distinguishing feature of our services at Web Crest is our undying commitment to proactive observability. We go beyond just developing the solution but ensure it's built with strong observability which means bringing in suitable tools like Axiom · Sentry · Langfuse for cost tracing. This would enable us to catch any possibility of regression potential by data rather than through conventional visualization which leads to quicker mitigation in case any issue arises.

@hoangnn90

Your project aligns closely with the type of AI and computer vision systems I've built over the past 12+ years in Python, machine learning, deep learning, NLP, and production AI deployments. For a project like yours, I would be particularly interested in improving detection recall, reducing fallback rates through stronger computer vision preprocessing and measurement pipelines, optimizing model orchestration and retry strategies, and introducing metrics-driven evaluation to continuously monitor extraction quality, latency, and cost. I have substantial experience building and deploying computer vision and machine learning systems on noisy real-world data and improving model accuracy, robustness, and operational performance in production environments.

@quan9824

As a seasoned Python engineer with an extensive background in computer vision and a niche in LLM pipelines, I'm highly confident in my ability to meet the unique needs of your project. Spanning from enhancing detection precision to reducing fallback rates, the areas you've mentioned dovetail seamlessly with my skill set. My earlier work in this domain involved improving accuracy on real-world inputs, particularly those with high levels of noise. My experience traverses multiple providers and utilizes several LLMs, including Anthropic Claude, Gemini, and Vertex AI's spatial detection. This diverse exposure has not only equipped me to optimize LLM cost and latency but also fortified my capacity to deliver structured tool-output as desired. With proficient knowledge of Python (in async/concurrent, staged pipeline architecture settings), I'm primed to ensure your pipeline stays robust by mitigating partial-page failures and introducing effective timeouts, retries, and keep-best logic parameters. To strengthen my pitch further, I would like to highlight being dynamically responsive and capable of making emergency modifications - underscoring my dedication to ensuring uninterrupted project progress. With a track record of 100% job completion rate and on-time delivery plus rave reviews from satisfied clients attesting to the quality of my work，籽 am here to turn your vision into reality. So don't go any further， choose me now!

@Aaleen110

I understand you need a Senior Python Engineer to enhance your existing production pipeline for extracting structured data from complex technical PDFs. My experience with vision LLMs and computer vision, specifically in object detection and feature tracing on document-like structures, has consistently led to a 15% improvement in extraction accuracy for similar projects. I will deliver a refined extraction module leveraging `torchvision` for spatial vision models and `LangChain` for integrating your LLM. The output will be structured JSON objects detailing detected instances, their bounding boxes, and calculated measurements (areas, lengths) as specified in the reference key. This will be integrated into your existing Python framework. What specific types of linear features and area measurements are most critical for your current accuracy improvements? Ready to start as soon as you confirm scope.

@sagarwal3005

I have extensive experience in web development, Node.js, React, and PHP, with a proven track record in Excel automation and accounting software. I specialize in improving accuracy and recall on dense, messy, real-world documents through computer vision technology. My expertise includes OpenCV, contour/skeleton tracing, and image geometry. With a strong background in LLM pipelines and structured-output systems, I am confident in my ability to drive down costs and latency without compromising quality. Let me take ownership of your Python pipeline and elevate its performance to new heights. Check out my portfolio for examples of similar successful projects.

@xuezhe315

Hello, This project is highly aligned with my background in Python, AI pipelines, computer vision, and document-processing systems. I've worked on PDF/image analysis workflows involving OpenCV, structured extraction, LLM orchestration, and production-grade backend architectures. What stands out is that your challenge is not simply calling a vision model—it's improving extraction quality, measurement accuracy, resilience, and cost efficiency across a multi-stage pipeline. That's exactly the type of optimization work I enjoy. I can contribute in several areas: Improving detection recall and precision on noisy technical documents Optimizing contour extraction, area measurement, skeletonization, and geometric tracing Reducing unnecessary LLM fallbacks through stronger CV heuristics and validation Hardening pipeline reliability with retries, idempotency, timeout handling, and keep-best logic Cost and latency optimization across multi-provider LLM workflows Enhanced observability using Langfuse, Sentry, and structured metrics My experience includes Python async systems, OpenCV-based image processing, AI-powered document analysis, and production data pipelines where accuracy and operational reliability were critical. I'd be happy to review sample documents, current architecture, and key bottlenecks to identify the highest-impact improvements first. Looking forward to discussing the project.

@kamran2012

I can help with this, I will improve detection recall on dense pages, tighten the CV measurement path, and reduce LLM fallback rate across your pipeline. For contour/area extraction on noisy inputs, I will implement adaptive preprocessing — morphological filtering tuned per-page density — before contour detection, which typically cuts false positives and sharpens skeleton tracing without touching the LLM layer at all. Questions: 1) What is your current fallback rate from CV to the LLM agent path? 2) Are you batching pages per LLM call or sending them individually? Ready to start whenever you are. Kamran

@alizain721

Dense technical PDFs usually fail because a single detection model is asked to do too many brittle tasks (layout, tiny-object recall, and measurement) without explicit fallback/validation paths — that’s why you’re seeing high fallback rates and cost creep. My approach: introduce a hybrid pipeline that layers a fast deterministic CV stage (OpenCV + light detector) for high-recall proposals, a specialized trained detector for precision, and a vision-LLM only for disambiguation and structured-tool orchestration. Add verification steps (geometry checks, scale-consistency rules) before invoking the LLM so you reduce calls and fallbacks. Suggested stack: keep Python async pipeline, add a small edge detector (YOLOv8/Detr-family or custom EfficientDet) for proposals, OpenCV/skimage for contour/skeleton tracing, lightweight local LLM/agent for prompt orchestration and Anthropic/Gemini only for edge cases. Use caching, prompt templating, and sample-based LLM batching to cut cost. For reliability: introduce partial-page checkpointing, idempotent job tokens, deterministic retries, and continuous regression tests with noisy synthetic PDFs; wire metrics into Axiom/Sentry/Langfuse for data-driven alerts. Similar work: I built CrowdAxis’ ETL/scoring pipeline handling messy external feeds, raising signal quality and lowering manual review — a comparable production mindset and testing discipline. If you’d like, I can audit a few sample pages and outline a 2-week plan. Quick question: can you share representative PDF pages and current fallback/error breakdown (detection misses vs measurement failures)?

@abdulhamidh2

Hola He leído su proyecto para asistencia técnica informática remota y creo que puedo ayudarle eficazmente. Entiendo la importancia de diagnosticar y resolver problemas de hardware y software rápido, además de mantener sistemas operativos y aplicaciones en óptimas condiciones para asegurar la continuidad operativa. Cuento con amplia experiencia en soporte remoto, diagnóstico detallado y mantenimiento preventivo, trabajando con diversas plataformas y configuraciones. Mi enfoque es claro: identificar la raíz del problema, aplicar soluciones rápidas y mantener una comunicación constante para que todo funcione sin interrupciones. ¿Podría contarme qué tipo de sistemas o equipos usa actualmente para adaptar mejor el soporte a sus necesidades? Saludos cordiales, AbdulHamid

@toriquldev123

Hello dear, Greetings from MD. Toriqul Islam! We are a dedicated Web Design & Development team with over 10+ years of industry experience. I’m Engineer Toriqul Islam, an experienced Computer Science & Engineering graduate from RUET. We specialize in building modern, scalable, and user-friendly digital solutions tailored to business needs. What I Offer We help businesses grow online by delivering: • Clean, modern, and responsive website designs • High-performance and scalable web applications • User-focused UI/UX for better engagement and conversion My Technical Expertise We work across a wide range of technologies, including: • Frontend: HTML5, CSS3, Bootstrap, JavaScript, jQuery, Angular, React • Backend: Node.js, PHP, Laravel, .NET, CodeIgniter, Ruby on Rails, Python • CMS & Platforms: WordPress • Database: MySQL, MongoDB • Mobile Development: React Native, Flutter, and more Why choose me? ✔️ Clean, optimized, and well-documented code ✔️ Reusable and scalable components ✔️ On-time delivery with complete requirement fulfillment We are confident in our ability to turn your ideas into a powerful digital product. Let’s discuss your project and make it a success. Looking forward to working with you! Best Regards, Md. Toriqul Islam