Computer Vision Engineer for PropTech Company Ideally office based or Hybrid

@ZohaibRoy

⭐⭐⭐⭐⭐ Lead Engineer for Computer Vision & LLM Systems ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a Lead Engineer in Computer Vision and LLM Systems. Look no further; Zohaib is here to help you! My team has successfully completed 50+ projects in computer vision and machine learning. I will create robust pipelines for floor plans and ensure compliance while enhancing system performance. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in computer vision and LLM systems, specializing in object detection, data pipelines, and architectural design. I also have a strong grip on Python, AWS, and production-grade systems, which ensures I can deliver an efficient solution. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Computer Vision ✅ Object Detection ✅ Geometric Reasoning ✅ LLM Architectures ✅ Data Pipeline Management ✅ Python Programming ✅ AWS Services ✅ Docker ✅ PostgreSQL ✅ Observability Tools ✅ Incident Response ✅ Mentorship Waiting for your response! Best Regards, Zohaib

@diektech

Hi, I reviewed the role and it's a very interesting challenge. The combination of computer vision, deterministic appraisal logic, and production-grade LLM architecture is exactly the kind of engineering problem I enjoy solving. What stood out most is your focus on architectural guarantees rather than prompt engineering. I agree that permission-scoped retrieval, strict schema validation, deterministic calculation engines, and post-generation validation are the right approach for a regulated environment. The LLM should narrate validated results, never generate or infer financial figures. I have experience building Python/FastAPI backends, Docker-based deployments, PostgreSQL, production API integrations, RAG pipelines, structured outputs, and observability. I also have experience designing scalable systems where reliability, monitoring, and maintainability are priorities from day one. A few questions: 1. Is the current CV pipeline based on traditional CV, deep learning models, or a hybrid approach? 2. Which vector database and LLM stack are you currently using? 3. Are you open to an initial contract engagement before moving into a longer-term role? I'd be happy to discuss the architecture in more detail and, if appropriate, sign an NDA before reviewing the existing implementation.

@Softeria

With over a decade of experience in artificial intelligence software development, data analysis and predictive modeling, I believe I possess the skills necessary to excel in this role. In particular, my expertise in Computer Vision precisely aligns with the needs of your project. Having worked extensively with object detection and segmentation models such as YOLO and Mask R-CNN, I have gained proficiency in working with structured imagery akin to floor plans, CAD drawings, and architectural layouts. This experience also extends to geospatial reasoning on top of CV outputs including polygon extraction, area computation, constraint-based spatial reasoning. In addition to my Computer Vision credentials, my competency in LLM/RAG systems is a relevant value-add for executing this position successfully. With hands-on experience building tool-calling architectures with hard output constraints and employing RAG pipelines for retrieval tuning, I have a thorough understanding of the methodologies involved in producing real-world solutions that adhere to compliance requirements. Moreover, my responsibilities as an AI Engineer have also equipped me with a strong sense of ownership and responsibility when it comes to ensuring product quality, monitoring uptime and swift incident response - all crucial aspects demanded by this role.

@Lats7

Hello, Your platform tackles exactly the kind of production AI challenges I enjoy solving. I'm Stefan from WebAIProTech, based in Australia, with over 10 years of software engineering experience and the last 5 years focused on computer vision, LLMs, RAG architectures, AI agents, and production AI systems. I've built AI platforms using FastAPI, Python, OpenAI, vector databases, structured tool-calling, and retrieval architectures where deterministic services remain the source of truth. I also have experience designing secure AI workflows, production observability, and enterprise SaaS systems. While I'm not UK-based, I'm comfortable working UK business hours, collaborating closely with distributed teams, and communicating clearly with both technical and non-technical stakeholders. If remote collaboration is an option, I'd welcome the opportunity to discuss how I can help strengthen your CV pipeline and Otto's production architecture. Best Regards, Stefan

@Hubready

Hello!, I am a US-based senior software engineer(frontend, backend, ecommerce, etc) with 15+ years of experience in Python, ML, PostgreSQL, Docker, Flask, FastAPI, computer vision, and deep learning. I read your project carefully. This looks like a Lead Engineer role where the goal is not just CV work, but building reliable AI systems that turn visual data into something useful, scalable, and decision-ready for a PropTech company. That is exactly the kind of problem I enjoy solving. My approach would be: 1. Review your data, workflows, and target outcomes 2. Define the CV/LLM pipeline and API structure 3. Build a Dockerized Flask/FastAPI backend 4. Add PostgreSQL storage and clear visual reporting 5. Test, refine, and prepare for production use Could you please clarify the following questions to help me better understand the project? 1. What type of visual data are you working with, and what is the main output you need? 2. Do you already have data/models in place, or should this start from scratch? 3. Is the focus on R&D/prototyping or a production-ready build? I’ve built similar AI and data products, including CV tooling, LLM-powered dashboards, and API-driven automation systems. I’m serious about the details and would like to make sure I understand the exact outcome before moving fast. If helpful, open a chat and I’ll map out the best path clearly. James Zappi

@chraheelbhatti

With over a decade of comprehensive ML experience under my belt, I am eager to own the critical facets of your Computer Vision and LLM systems. My proficiency with object detection algorithms, such as YOLO, Detectron2, and Mask R-CNN, combined with the ability to reason geometrically upon their output gives me a strong advantage in delivering quality solutions for your floor plans and plot data segmentation needs. Furthermore, having engaged in OCR development on low-quality scanned/PDF inputs, I understand the importance of fail-safe methods that identify errors rather than perpetuate them quietly. Overall, my academic journey and professional career illustrate a deep understanding of AI systems like yours. My previous works on ML-powered systems for state institutions—a context that requires uncompromising standards—are proof of my ability to rise-to-the-task in ensuring operational stability. Working in synergy with your existing engineers, I am confident I can provide not just mentorship but substantial technical direction as we continue to extend the core differentiated IP of your product: the CV thesis. In conclusion, hiring me means not only engaging a knowledgeable engineer but also a reliable partner to drive this project towards even greater success;

@trm66614

I am excited about the opportunity to lead the Computer Vision and LLM systems for your innovative property intelligence platform. With a strong background in computer vision and LLM architectures, I can effectively oversee the development pipeline that converts raw floor plans and plot data into structured geometric representations, ensuring robust outcomes for your users. My experience in objects detection, geometric reasoning, and creating resilient data pipelines aligns perfectly with your needs. Furthermore, I am well-versed in compliance requirements which will ensure that our LLM architectures meet the regulatory standards necessary for successful deployment. I understand the importance of owning the production environment and guaranteeing the integrity of the appraisal engine, especially during crucial pilot phases. My leadership will not only focus on technical aspects but also on mentoring your existing engineers, fostering an environment of growth and collaboration. Can you elaborate on the specific compliance requirements for the LLM outputs? Together, we will enhance Otto’s capabilities, ensuring a reliable and compliant platform that exceeds user expectations. I look forward to contributing to your team's success in this exciting journey.

@melopgabriel

Hello, Your mission of transforming raw floor plans into structured, appraisal-ready data resonates with my experience building production-grade computer vision and LLM systems with high regulatory and architectural demands. I’ve previously owned end-to-end vision pipelines for technical imagery—extracting geometry, segmenting complex CAD/floor plan features, and ensuring reliable area computation, even with degraded or scanned inputs. My work integrating OCR and robust error handling minimizes silent failures and maintains data integrity. On the LLM/RAG side, I’ve implemented permission-scoped retrieval and schema-enforced output in regulated domains, ensuring that context leakage between user tiers is structurally impossible, not just prompt-suppressed. My approach includes strict post-generation validation, adversarial red-teaming for compliance, and enforcing output constraints at the architecture level. I’ve built retrieval-and-narrate systems where the model only surfaces pre-computed results, never generating its own calculations. For infrastructure, I’m comfortable carrying the pager, having built and operated Python (FastAPI/Flask) backends with Docker, PostgreSQL, and observability stacks in live production. My focus is on making failure modes explicit and fixing pipeline fragility at the root. I’m confident I can help you harden your pipeline, close compliance gaps, and extend your core IP. Let’s discuss your current architecture and where the biggest pain points are. Best regards, Gabriel

@mobasherreza10

Hi there, With over a decade of expertise in the realm of Computer Vision (CV) and the Langage Learning Model (LLM), I am confident that I am the best-suited freelancer for your project. My CV proficiency includes object detection, segmentation, geometric reasoning, and OCR reliability, which comprise all key elements you mentioned for this position. Having produced striking results with CV in varied industries including satellite and aerial, I can channel my knowledge into intelligently deriving extendible development envelopes from floor plan geometry akin to your project's core requirements. Moreover, my experience with production-level LLM architectures in adhering to hard output constraints plays a vital role in making your compliance logic structurally unbreakable. Your project's need for schema-enforced, denylist-constrained generation for regulatory purposes and vector DB retrieval tuning resonates well with my professional experience. It would be an absolute pleasure for me to take ownership of refining your data pipeline fragility, ensuring uptime, monitoring, and responding to incidents while setting up robust observability stacks.