Hire Developar

@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.

@quan9824

Hello, I am an AI/ML Engineer and Full-Stack Developer with hands-on experience building end-to-end AI solutions, from research prototypes to production-ready services. I specialize in Python-based machine learning systems, deep learning model development, and deploying AI applications through scalable cloud and containerized environments. Recently, I worked on AI projects involving computer vision, object detection, active learning pipelines, and model optimization. I have designed training workflows using PyTorch/TensorFlow, handled data preprocessing and feature engineering, evaluated models with business-focused metrics, and created reproducible pipelines with clear documentation. I am also experienced with FastAPI-based ML services, Docker deployments, GPU-based training environments, and integrating AI models into larger software systems. For Ednarex, I can help transform your ideas into working AI prototypes by: * Designing and implementing suitable ML architectures based on your business objectives. * Building clean Python training pipelines with experiment tracking, validation, and reproducibility. * Optimizing models for practical deployment requirements. * Packaging AI services using containers and integrating them with cloud infrastructure. * Providing clear technical documentation covering architecture decisions, evaluation results, and future improvements. I am available to start immediately.

@salesapple

Hi, I'm excited about the opportunity to help build AI-powered products at Ednarex. With 9 years of software development experience and strong expertise in Python, AI/ML solutions, cloud platforms, and scalable system architecture, I can take ideas from concept to production-ready services. I have hands-on experience with TensorFlow, PyTorch, MLOps practices, API development, Docker, and AWS/Azure, delivering clean, maintainable, and well-documented solutions. I can build and validate end-to-end ML prototypes, define meaningful evaluation metrics tied to business goals, and collaborate closely with product and engineering teams to ensure successful deployment. I would welcome the opportunity to contribute to your early engineering team and help turn innovative ideas into impactful AI products. Thanks

@ashinnd84

Hi there, we are a team of Full Stack Web and Mobile App developers and we can do this project in no time. Thanks Ashish Kumar.

@ambientinfotech

Leveraging over a decade of experience in web and mobile app development, my team at Ambient Infotech has successfully completed more than 1200 projects and can offer the required depth to transform your AI concepts into functional marvels. With an official partnership with Odoo, we possess a deep command of Python for data processing, nuances in ML frameworks like TensorFlow, PyTorch, and can ace cloud technologies like AWS or GCP. Our expertise is not limited to just writing code. We come with practical hands-on knowledge and follow industry best practices in developing ERP, CRM, and eCommerce solutions that make a real difference for businesses. Focusing on operational efficiency and automating workflows that drive results is our thing. Building an end-to-end prototype of AI-powered systems is not just about writing code. It's about strategizing, testing, documenting choices, evaluating meticulously against professional standards—all of which we excel at. Promising full-stack proficiency, from well-documented Python

@alluankit01

Hi, I can support Ednarex as an AI/ML Engineer and help take early product ideas from concept to working, deployable AI services. The best solution is to first understand the product goal, available data, business KPI, baseline target, and deployment environment. I’ll then design the ML approach, build the data pipeline, train and validate models, track experiments, and package the best model as a clean Python service ready for cloud deployment. I’m comfortable with Python, PyTorch, TensorFlow, scikit-learn, feature engineering, model evaluation, experiment tracking, Docker, FastAPI, AWS/GCP/Azure, microservices, MLOps, unit testing, and technical documentation. Deliverables will include: * End-to-end ML prototype * Reproducible training pipeline * Data processing and feature logic * Model training and validation * KPI-based evaluation metrics * Python microservice/API * Docker/cloud deployment support * Clean documented source code * Architecture and next-step write-up * Testing and linting support I’ll focus on practical AI systems, not only experiments, so the prototype is reliable, measurable, and ready to evolve into a production product. Best regards Ankit

@jaroslavcaprata

You're looking for someone who can own the full lifecycle of an ML product, not just train a model. The real challenge is building a reproducible pipeline that moves smoothly from experimentation to deployment while remaining maintainable as the product evolves. I can contribute across the entire workflow, from data processing and model development in Python to API integration, containerization, cloud deployment, and production-ready services. My approach is to build modular pipelines with clear experiment tracking, versioned datasets and models, automated testing, and deployment practices that make future iterations straightforward rather than rebuilding from scratch. For a green-field project, I would first establish a solid architecture for data ingestion, training, evaluation, and serving so new models can be added without disrupting the platform. I also place emphasis on documentation and clean code to ensure collaboration with product and cloud engineers remains efficient. I'm interested in long-term product development rather than delivering a one-off prototype. One question: what type of AI product is your first milestone focused on (LLMs, computer vision, recommendation systems, forecasting, or another domain), and do you already have training data available?

@dushyant317

Hi, I can build production-ready AI/ML solutions from research to deployment, delivering scalable Python-based models with MLOps best practices, cloud integration, reproducible training pipelines, and clean, well-documented code.

@thiagofoz87

Your scope indicates you need more than model experimentation — you need an engineer who can turn ML concepts into reliable services that are deployable, testable, and maintainable from day one. The combination of Python, cloud infrastructure, containerisation, and reproducible pipelines is where I can contribute effectively. I can help structure the project around three core layers: - data processing and feature pipelines in Python; - model training/evaluation with reproducible experiments; - deployment-ready inference services using containerised microservices on AWS. My approach would be to first define measurable validation metrics tied to the target business outcome, then implement a clean training pipeline with experiment tracking, versioned datasets/models, and automated validation steps. For deployment, I would structure the solution as lightweight services with clear observability, logging, and scalable infrastructure patterns. I also place strong emphasis on code quality and maintainability: linting, unit tests, modular architecture, and documentation are part of the delivery, not an afterthought. Given the green-field nature of the project, establishing a solid engineering foundation early will make future iteration significantly faster. I’m available to start immediately and can deliver the first working prototype incrementally so validation can begin early instead of waiting for a final handoff.

@irfanui

Hi, I reviewed your project: Hire Developar. Your requirement involves Firebase, Microsoft Azure, and/or Power Platform integration—not a generic Laravel build. I can design the required data flow, connect APIs and cloud services, automate business processes, and implement secure synchronization with clear logging, monitoring, and error handling. I can work with Firebase/Firestore, Cloud Functions, Azure Functions, Azure Data Factory, Azure SQL, Dataverse, Power Apps, Power Automate, Power BI, .NET/C#, REST APIs, authentication, and production deployment. I will first confirm your current architecture, data sources, required workflows, access model, and acceptance criteria, then provide a practical implementation plan. Portfolio: https://www.freelancer.com/u/irfanui Regards, Mohammad 4th Dimension Partners

@heenafullstacken

Bringing Firebase, Azure, and Power Platform together successfully depends on a clean data flow and reliable automation. For Hire Developar, I can connect the required services, map and synchronize records, build secure functions or workflows, and include validation, monitoring, retries, and useful error logs from the beginning. I work across Firestore, Cloud Functions, Azure Functions, Data Factory, Azure SQL, Dataverse, Power Apps, Power Automate, Power BI, and .NET integrations. If you share the current systems, data direction, update frequency, and desired final outcome, I can turn them into a clear implementation roadmap. See my work: https://www.freelancer.in/u/heenafullstacken Warm regards, Heena A Plus IT House

@Ashoksahu001

Hello, Ednarex's vision of building AI-powered products from the ground up is exciting, and I'd love to contribute by developing scalable, production-ready ML solutions that move quickly from prototype to deployment. I have experience building Python-based backend applications, AI integrations, API-driven systems, and cloud-ready architectures with a strong focus on clean, maintainable code. I can collaborate closely with product and engineering teams to develop reproducible ML pipelines, deploy models as scalable services, and ensure they're optimized for real-world performance and future growth. Why hire me? I focus on building robust, well-documented solutions that are easy to maintain, test, and scale. From experimentation to deployment, I emphasize code quality, automation, and reliable delivery. What I'll deliver: • End-to-end ML prototype with documented Python code • Reproducible training pipeline • Model evaluation with agreed performance metrics • API/service deployment support • Technical documentation covering architecture, evaluation, and next steps • Clean, tested, maintainable code following best practices I'm available to start immediately and would be happy to discuss your technical roadmap, onboarding process, and how I can help accelerate the first production-ready AI services for Ednarex.

@EthanKCK

Ethan here, from South Africa. I've read through your project and I'm definitely interested in assisting you. ABOVE THE REST SUCCESS I understand you're looking to build AI-powered products that tackle real-world problems. In a recent project, I developed a machine learning model that improved predictive accuracy by 30% for a client in the healthcare sector. To approach your project, I'd start by designing an end-to-end prototype using TensorFlow for model training and experiment tracking. Then, I’d set up a reproducible training pipeline on AWS to ensure smooth deployment. Quality is my priority—I'll deliver clean, maintainable code and provide thorough documentation on architecture choices and evaluation metrics. What you truly need is a reliable solution that meets your business KPIs while allowing for future scalability. Please feel free to reach out so we can connect and further explore how I can contribute to your project's ABOVE THE REST SUCCESS. Kind regards, Ethan

@bojans9

Namaste from Serbia to Udaipur I get that you need a Python ML engineer to turn a sketch into a deployable model service I will create a TensorFlow prototype, add MLflow tracking, containerise with Docker and push to Azure GPU meeting your validation baseline You will recieve clean lint‑free code, unit tests and a short technical write‑up I recently built a recommendation engine for a retail client within 120000 INR budget in four weeks NDA prevents sharing the repo but I can show a demo version I can start next Monday What is the size and format of your training data Which KPI defines success for you Do you have an existing test suite or should I write basic tests Happy to discuss next steps

@ridwaan6254

IF YOU'RE NOT HAPPY, YOU DON'T PAY. I recently completed a project where I developed an AI model that improved prediction accuracy by 30 percent, directly impacting the client's business metrics. I understand you need an AI/ML Engineer who can take concepts to a running service, focusing on clean, maintainable code. I will leverage my deep command of Python and experience with TensorFlow and PyTorch to build and validate models aligned with your business KPIs. My familiarity with AWS ensures seamless deployment and smooth operation of your models. I focus on good planning, clean and maintainable code, clear communication, on time delivery, and reliable long term solutions. I am eager to contribute to pushing AI boundaries with your team. If this aligns with your project, feel free to reach out to discuss scope and pricing. WORST CASE SCENARIO YOU WALK AWAY WITH A FREE CONSULTATION.

@kirtii2427

Hi, I’m interested in contributing to Ednarex as an AI/ML Engineer. My background includes Python development, AI-powered applications, API integration, backend systems, and deploying AI functionality into real-world web applications. I’ve previously built an AI-powered legal assistance platform using Python, Flask, LangChain, and OpenAI APIs, where I worked on AI integration, backend architecture, data handling, and converting AI capabilities into a usable product. I’m comfortable with Python-based development and eager to work across the full AI lifecycle—from experimentation and evaluation to APIs, containerisation, deployment, and continuous improvement. I’m particularly interested in the green-field nature of this role and the opportunity for long-term collaboration.