Backend Developer for Similarity Algorithm -- 2

@irsolutions

With over a decade of experience in backend development, specializing in similarity algorithms and high-scale systems, I understand your need for a proprietary weighted similarity engine for your tech startup. My background in building high-complexity systems, such as serving over 1 million users, directly applies to the challenges of implementing a multi-channel weighted similarity algorithm and a two-stage architecture for your project. A strategic insight for ensuring scalability in this project is to design the system with a flexible configuration approach for injecting weights at runtime, as opposed to hard-coding them. My past success in delivering high-performance solutions, like the Telegram Mini Apps, showcases my capability to handle projects of this complexity. I encourage you to reach out to discuss the roadmap for your similarity algorithm project. Let's collaborate to bring your vision to life effectively and efficiently.

@corpmember29

Hello, I read your requirements carefully and understand that you already have a complete technical specification and need a backend developer to implement it accurately without redesigning the system. I have 11+ years of experience in Python/backend development, including scoring, similarity, matching, and recommendation-based systems. I can implement the multi-channel weighted similarity engine with runtime-configurable weights, compatibility gate, ranking layer, read-only external graph integration, and REST API returning structured JSON. I will follow your specification precisely, build the seven defined unit test cases, optimize the implementation toward the <50ms response target, and provide clean documentation and README for handover. I am comfortable signing an NDA and IP assignment agreement before reviewing the full specification and understand that Australia-based availability is mandatory. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. I eagerly await your positive response. Thanks, Christina

@technosystem

With a strong grasp of similarity, scoring and recommendation algorithms gained from years of backend development experience, I am certain that I possess the perfect combination of skills required for your project. My portfolio includes successfully building high-performance apps in Python and other languages, such as those which navigate complex social connections and make informed market recommendations. I understand the significance of performance in your project. Whether it's handling multiple weighted factors, implementing a two-stage architecture or achieving sub-50ms response time, my experience has thought me various ways to write elegant code for efficient data processing. Moreover, my work on data-intensive applications allows me to meticulously manage read-only query interfaces - a key aspect of your project. Another advantage I bring is my eye for detail which aligns well with the need for clean documentation and quality READMEs. Rigorous testing being inseparable from dependable solutions, I guarantee to leave no stone unturned in crafting an extensive unit test suite based on the seven predefined cases provided. Tailored to meet your unique vision, I toute seamless end-to-end development that mages well to complete specifications and injects runtime configuration rather than hardcoding alongside adherence to theIP assignment agreement

@Lats7

Hi, I’m Stefan, a Melbourne-based Senior Backend / AI Engineer with 10+ years of software engineering experience. I have strong experience building similarity, scoring, ranking, recommendation, and vector-search systems using Python and production APIs. I’ve worked extensively with embeddings, semantic similarity, weighted retrieval, ranking pipelines, RAG systems, and high-performance backend services. I’m comfortable implementing a defined algorithm precisely from a technical specification rather than redesigning the system. For this project, I would implement the two-stage compatibility gate and ranking layer, keep all weights runtime-configurable, integrate the read-only graph interface, expose a clean REST/JSON endpoint, and build comprehensive unit tests around the seven defined cases. I’d also profile and optimize the implementation to meet the <50ms response target. I’m based in Australia and comfortable signing an NDA and IP assignment before reviewing the specification. I’d be happy to discuss the project over chat. Best Regards, Stefan

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Data Processing, Algorithm, Backend Development, REST API and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@gotucat

Hi, I reviewed the request to build a weighted similarity engine, driven by injected configuration, with a compatibility gate then ranking layer and a REST API returning structured JSON. I’ll implement the Python backend to run the multi-channel similarity algorithm, call the read-only query interface to your external data graph, and expose a REST endpoint that supports your runtime weights without hard-coding. I’ll focus on correctness with the full unit test suite for seven test cases, clean documentation and a clear README, and performance under 50ms per response. Let’s discuss here now.

@mayr81

Build a proprietary weighted similarity engine exactly to your contractor-ready spec, no architecture design required. Implement a two-stage flow: a compatibility gate followed by ranking, with runtime-injected weights (not hard-coded) and a strict REST API returning structured JSON match scores. Use your read-only external data graph via controlled query interfaces, and ensure performance under 50ms per response through efficient backend logic and testing. Deliver a complete unit test suite covering all seven defined cases, plus clean documentation and a README aligned to the provided specification. I will sign the required NDA and IP assignment agreement before receiving the full spec, and I can work with your Australia-based requirement.

@vishal1145

Your two-stage architecture will fail under load if the compatibility gate isn't indexed properly. A sequential scan across thousands of profiles before ranking will push response times past 500ms, not 50ms. Quick questions - are you caching gate results between requests? And what's your expected concurrent user volume at launch? Here's the architectural approach: - SIMILARITY ALGORITHM: Build a vectorized scoring engine using NumPy to batch-process profile comparisons in under 10ms per operation, keeping you well under the 50ms target. - REST API: Design a FastAPI endpoint with Pydantic validation that returns structured JSON and handles runtime weight injection without requiring redeployment. - UNIT TEST SUITE: Write pytest fixtures covering all seven test cases plus edge cases for missing data, ensuring the gate logic doesn't break when profiles have incomplete fields. I've built recommendation engines for two Australian SaaS companies that process 200+ matches per second. Let's schedule a brief technical call to confirm your data graph schema before I sign the NDA.

@tnt42010

Hello, I have strong experience with **Python backend systems, scoring engines, recommendation/similarity logic, REST APIs, performance optimization, and test-driven development**. Your architecture is clear: implement the completed specification as written, including the compatibility gate, weighted ranking layer, runtime-configurable weights, read-only graph queries, structured JSON output, and the seven defined test cases. I would focus on: * Deterministic, explainable scoring logic * Clean separation between gate, ranking, data-access, and API layers * Runtime-injected configuration * Efficient graph reads/caching where appropriate * Profiling against the <50 ms target * Comprehensive unit tests and concise documentation I’m comfortable signing NDA/IP agreements and working from a strict contractor-ready specification. One important point: I’m based in Türkiye, not Australia, so I don’t meet your stated location requirement. If that requirement is flexible, please contact me and I’d be happy to discuss the implementation.

@CorporateRD

As a backend developer with a proven track record in python development and building high-performance REST APIs, I believe I would be a perfect fit for your project. I have a strong understanding of similarity, scoring, and recommendation algorithms which aligns precisely with your requirements. Not to mention, I bring along extensive experience in developing architectures that align closely with the two-stage architecture model you are aiming for. Moreover, my team's AI-first approach and Vibe Coding methodology will not only ensure we follow your contractor-ready technical specification to the T but also guarantee swift product delivery. We not only generate code as per the specifications but also design comprehensive solutions that are ready for real users and long-term growth. Our commitment to clean documentation, a full unit test suite, and performance optimization makes us well-equipped to ace the challenges of this project. Above all, we understand what it takes to turn an idea into a scalable digital product without any compromise on efficiency or security. Our clients choose us because we offer more than just code-writing; we provide long-term technical partnerships and ongoing support for all projects we undertake. Let's join forces and transform your vision of a proprietary weighted similarity engine into an exceptional reality!

@sekai1116

BACKEND DEVELOPER — WEIGHTED SIMILARITY ENGINE I can build the similarity engine directly from your completed technical specification without changing the defined architecture or algorithm. I’m comfortable with Python backend development, weighted scoring systems, similarity logic, REST APIs, structured JSON responses, external read-only data sources, and comprehensive unit testing. I’ll implement the two-stage compatibility gate and ranking layer with runtime-injected weights rather than hard-coded values. The seven specified test cases will be covered, with clean documentation and a practical README. I’ll also keep performance in focus to meet the sub-50ms response target where the underlying data access allows it. I’m based in Australia and can sign the required NDA and IP assignment before reviewing the full specification. I’m ready to review the requirements and begin with the agreed fixed scope.

@AhmadXpert2410

Hi there, I have 3 years of professional experience in Python, Data Science, and backend-focused ML development, with hands-on experience building predictive models, scoring workflows, data-processing pipelines, and production ML services. My work includes feature engineering, statistical analysis, model evaluation, and working with large datasets, which aligns well with implementing a specified weighted similarity and scoring engine. I can implement your two-stage compatibility and ranking architecture exactly against the provided specification, keeping weights runtime-configurable rather than hard-coded. I also have experience developing REST APIs with FastAPI and building production data/ML pipelines using Python, SQL, Pandas, and NumPy, with a strong focus on clean JSON outputs, testing, performance, and maintainable code. I understand this is an implementation-focused assignment rather than a system-design exercise, and I’m comfortable working from a completed specification, writing the required unit tests, documenting the implementation, and optimizing the endpoint toward the sub-50ms target. I’m also comfortable signing an NDA and IP assignment before reviewing the full specification. Regards, Ahmad

@jonasdedzo

Hi, Weighted similarity algorithm is not the hard part here. The real challenge is hitting the <50ms target consistently while keeping the compatibility gate and ranking layer fully driven by runtime configuration rather than code changes. I'd build directly to the specification with Python and a clean separation between the scoring engine, graph-query layer, and REST API. One detail I'd pay attention to early is avoiding repeated graph lookups during ranking; caching and precomputed normalization often make the difference between passing and missing latency targets. I've also worked on AWS data-processing systems where response time and deterministic scoring mattered more than feature count. Is the external data graph exposed through a single query interface, or are there multiple data sources behind it that the similarity engine must coordinate? Looking forward to working with you.

@ssquare489

Your weighted similarity engine is a strong technical fit for my backend experience, particularly the configurable scoring layer, compatibility gate, read-only graph queries, deterministic test cases, and sub-50ms response target. However, I want to flag one constraint upfront: I am not based in Australia. Technically, I would approach the implementation exactly from your completed specification rather than redesigning the model. I’d keep runtime weights configuration-driven, isolate the compatibility gate from ranking logic, make graph access read-only behind a clean abstraction, and profile the REST path against the 50ms target. The main priorities would be clean implementation and maintainability so the seven supplied test cases remain deterministic and future scoring changes do not require invasive code changes. My background includes Python/backend systems, AI-driven applications, scoring and analytics workflows, API integrations, and production systems where structured outputs and performance matter. I’m also comfortable working under NDA and IP assignment terms. If the Australia requirement refers specifically to contractor residency rather than working hours or legal contracting eligibility, then I would not qualify.

@longngoc2307

==== Hi - Truong here ==== "WEIGHTED SIMILARITY ENGINE BACKEND" — you need the completed scoring specification implemented accurately, tested, and fast enough for sub-50ms responses. I’d implement the two-stage pipeline exactly from the specification, keeping all weights runtime-configurable rather than embedding them in the algorithm. I’d isolate the external graph access behind a read-only interface, return deterministic structured JSON, and cover the seven defined cases plus edge cases around missing fields, zero weights, and gate failures. Can you confirm whether the external data graph already exposes a documented API and whether the 50ms target includes that graph query? Looking forward to work with you

@ciss26

As a Full Stack Developer fluent in Python and with an impressive background in developing similar projects, I strongly believe that I am equipped with the skills necessary to tackle this challenge. Throughout my career, I have worked on multiple projects that involve similarity, scoring, and recommendation algorithms. This means I understand the nuances of building an algorithmic-driven project like yours. Moreover, my experience is not restricted to backend development only. I have extensive skills in frontend development as well. This means that not only can I build a performant backend to meet your project's needs, but I also appreciate the impact that a beautifully designed and intuitive frontend can have on user experiences. Lastly, as someone who has delivered successfully across a variety of industries and scales - from healthcare to e-commerce to logistics – I believe my ability to adapt and quickly learn new systems sets me apart. To further reiterate my commitment to this project, I am more than willing to sign an NDA and IP assignment agreement. Let's combine our expertise to build the exceptional weighted similarity algorithm your startup needs!

@Desmonds9

Hello! We are an experienced agency with a skilled team of backend developers and over 8 years of experience in building complex algorithms. Our expertise directly aligns with your project to develop a proprietary weighted similarity engine. We understand that you need a robust multi-channel similarity algorithm to compare user profiles, complete with a two-stage architecture and a REST API for structured JSON output. Our team is ready to help you achieve a high-performance solution that meets your technical specifications. To execute this project, we will leverage Python for backend development, implement a structured query interface, and ensure a comprehensive unit test suite is in place. Clean documentation will accompany the project to provide clarity and ease of use. Deliverables: - Multi-channel weighted similarity algorithm - REST API endpoint for JSON output - Comprehensive unit test suite - Clean documentation and README We are ready to start and deliver the results you need efficiently. Best regards, Ezra

@sanjana22kr

hello there , I can build your weighted similarity and matching engine directly from the completed technical specification, without unnecessary redesign. Relevant Experience • Strong Python/backend development experience • Similarity scoring, weighted algorithms and recommendation/matching logic • Two-stage scoring/ranking pipelines • REST APIs with structured JSON responses • External data/API integrations and read-only data access • Configuration-driven weights rather than hard-coded logic • Unit testing and edge-case validation • Performance optimisation for low-latency API responses I can implement the compatibility gate ,ranking layer ,final match score architecture exactly as specified, integrate with your external data graph, and cover all seven defined test cases. Deliverables • Production-quality backend implementation • REST API endpoint • Complete unit test suite • Performance optimisation targeting <50ms response time • Documentation + README • Clean, maintainable code I’m comfortable signing the NDA and IP assignment before receiving the full specification. Availability: Immediate Timeline: I can confirm the exact delivery schedule after reviewing the specification and existing data-graph interface. I’d be happy to review the specification and get started immediately. looking forward to work with you thanks

@TechWagger2026

With over a decade of experience in web and mobile app development, I bring a comprehensive range of skills that are ideal for your project. I have an extensive background in both backend and frontend development, with Python being one of my core strengths. My database expertise with SQL, MySQL, PostgreSQL, Oracle, MS Access, and MongoDB will be invaluable for your architecture. Additionally, I have a solid understanding of similarity and scoring algorithms which aligns well with the needs of your weighted similarity engine project. Throughout my career, I have successfully developed over 400 projects and ensure to provide end-to-end solutions just like you're looking for. I have worked with diverse industries including e-commerce, education, and medical which has given me a robust adaptability quotient making me proficient at injecting runtime configurations as per unique project requirements - a crucial skill required for this job. Lastly, my top-notch organizational skills allow me to document meticulously while creating clean code - therefore you can expect clean documentation along with the delivered project! I am also well-versed in delivering under-crunch timelines without compromising on the quality benchmarks. I'm ready to help bring your proprietary weighted similarity engine to life speedily and robustly!