AI/ML Engineers for Scalable Platforms

@diektech

Hi — Elias here from Miami. I see you're expanding your engineering group for new AI/ML products. The goal seems to be building scalable platforms that can adapt to evolving needs while integrating various technologies. What usually matters most here is ensuring the architecture can handle growth without compromising performance or maintainability. A common issue in systems like this is managing dependencies across different tech stacks, which can introduce complexity. The tricky part is usually establishing robust workflows that allow for seamless collaboration among team members. My approach would involve breaking down the system into modular components, ensuring each part is independently scalable. I'd focus on creating a clear API structure to facilitate communication between components, which helps future-proof the system. Additionally, I have experience with similar projects that required careful integration of AI and machine learning functionalities. A few questions to better understand the scope: Q1 – What user roles and permissions do you envision for these platforms? Q2 – Are there specific integrations with existing systems that we need to consider? Q3 – What are your scalability expectations as user load increases? Happy to go through the details and suggest the best technical approach. Looking forward to hearing from you.

@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

@ahmadayaz

I am a Machine Learning expert skilled in Python and experienced in optimizing model training on AWS GPU clusters. With expertise in designing reproducible ML pipelines and hyperparameter tuning using Bayesian optimization, I have achieved a 92% accuracy in sentiment analysis on large datasets. I excel in collaborating with diverse tech stacks like Rust and Ruby on Rails and have used AWS SageMaker and Lambda for efficient model training and scalable prediction services. Let's discuss how I can help drive performance and enhance your AI projects.

@koolvishruth

Hello Sir, I have 7 years of experience in Python, machine learning, AI solutions, AWS, data pipelines, APIs, and production automation. I will take ownership of the complete model-training workflow, including data preparation, reproducible PyTorch pipelines, fine-tuning, performance optimisation, testing, deployment, and monitoring. A recent project involved Llama2-based sentiment and review analysis for large-scale e-commerce data using Python and AWS. The exact dataset size and performance metrics are confidential, but I will share the relevant figures privately. My preferred optimisation approach is mixed-precision training with efficient batching and gradient accumulation because it reduces GPU memory usage, training time, and AWS costs while maintaining model accuracy. I have worked with AWS EC2, S3, Lambda, IAM, CloudWatch, Bedrock, Docker, FastAPI, Airflow, and CI/CD workflows. I will deliver a fully functioning pipeline, clean documentation, unit tests, model versioning, robust APIs, and measurable improvement against your baseline benchmarks. Ready to discuss your dataset, current baseline, and AWS architecture

@corpmember29

Hello, We completely understand your requirement for an AI/ML engineer to build and optimize scalable machine learning pipelines, improve model performance on AWS GPU infrastructure, and deploy production-ready ML solutions integrated with modern backend systems. I have 10+ years of experience in AI/ML, Python, PyTorch, TensorFlow, MLOps, AWS, and scalable cloud architectures. I have worked on model training, fine-tuning, distributed training, data pipelines, model optimization, deployment, monitoring, and API integration for production environments. I can deliver reproducible ML pipelines with comprehensive documentation, unit tests, and measurable performance improvements while collaborating seamlessly with full-stack and cloud engineering teams. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. WE WILL WORK WITH AGILE METHODOLOGY AND WILL GIVE YOU ASSISTANCE FROM ZERO TO PRODUCTION DEPLOYMENT. I am available on desk as per your convenient time zone and will work on your project until you are satisfied with my work. I eagerly await your positive response. Thanks, Christina

@devendrathakur12

Hi, I recently worked on an AI project where I built the complete training pipeline, optimized GPU training, and deployed the final model through Python APIs for production use. The focus was on reproducible training, faster experiments, and reliable model performance. I can help build efficient ML pipelines, fine-tune models, optimize AWS GPU workloads, and prepare everything with clean documentation and tests. I also enjoy working with backend teams so models move smoothly into production instead of staying in notebooks. One question: are you currently training custom models from scratch, or is the main focus fine-tuning existing foundation models? Looking forward to building AI that is both accurate and practical. Best regards, Dev S.

@Hubready

Hello!, I am a US-based senior software engineer(frontend, backend, ecommerce, etc) with 15+ years of experience building production systems in Python, Ruby on Rails, ML, Rust, and API development. I read your post carefully, and what stands out is that you’re not looking for a “feature coder” but someone who can help build scalable green-field products that blend full-stack web with AI/ML the right way. I’ve built SaaS platforms, data pipelines, automation tools, and high-traffic systems where clean architecture, speed, and maintainability matter. My approach would be: 1) clarify product goals and technical constraints, 2) design the architecture and data flow, 3) build the core API/backend and ML components, 4) wire up the frontend, and 5) test, harden, and deploy so it can grow without pain later. Could you please clarify the following questions to help me better understand the project? 1) Which product is the highest priority for the first milestone, and what does success look like for v1? 2) Do you already have preferred models, datasets, or an existing codebase, or should everything be designed from scratch? 3) How tightly do you want the ML layer integrated at launch: lightweight automation, or a deeper inference/workflow system? Relevant work I’ve done includes a trading analytics platform, an internal AI ops dashboard, a Python/Rails SaaS app, and an API-driven automation system.

@quan9824

Hello, I am excited about the opportunity to join your AI engineering team and take ownership of the complete machine learning workflow, from data pipelines and model training to optimization and production deployment. I have experience building Python-based ML systems using PyTorch and TensorFlow, including dataset preparation, reproducible training pipelines, model evaluation, fine-tuning, hyperparameter optimization, and GPU performance tuning. I focus on improving model accuracy while reducing training time and infrastructure costs. Recently, I worked on computer vision projects involving large image datasets, object detection/segmentation models, and active learning workflows. I built end-to-end pipelines covering data preparation, training, evaluation, inference optimization, and deployment integration. My preferred optimization techniques are mixed-precision training, efficient data pipelines, learning-rate scheduling, transfer learning, and profiling GPU utilization. These methods help accelerate experiments, improve resource efficiency, and achieve better model performance. I can deliver a clean, tested, and reproducible training pipeline with documentation, experiment tracking, and measurable improvements over baseline results. I enjoy building AI products from the ground up and working closely with engineering teams to bring models into real-world applications.

@gsantharam7088

Hi Sir/Madam, I have **6+ years of experience** in **Python development, AI/ML, cloud deployment, and scalable backend systems**. I have worked on AI-powered applications, automation tools, RAG systems, LLM integrations, and production APIs, and I'm comfortable building reproducible ML workflows that integrate seamlessly with modern web platforms. I can develop end-to-end **PyTorch/TensorFlow** training pipelines, including data preprocessing, training, evaluation, hyperparameter tuning, model versioning, and deployment on **AWS**. My focus is on optimizing GPU utilization, reducing training costs, improving inference performance, and creating maintainable pipelines with proper testing and documentation. I also have experience with **Ruby on Rails, Django, FastAPI, REST APIs, Docker, PostgreSQL, and AWS**, making collaboration across full-stack teams smooth. A recent project involved building an AI-powered document analysis pipeline using Python and LLMs with automated data processing and API integration. I frequently use techniques such as **mixed-precision training and efficient data loading** to improve training speed while reducing GPU memory usage. For AWS, I have worked with **EC2, S3, RDS, IAM, CloudWatch, and Docker-based deployments**. I'm available to start immediately and would be excited to contribute to your long-term AI initiatives.

@kabirchy

Hello, I understand you're looking to enhance your engineering team with expertise in machine learning for developing scalable platforms. Tackling both model training and optimization in Python is right up my alley. I can provide a robust solution to your needs by leveraging my substantial experience in AI and full-stack development. I'll begin by evaluating your current infrastructure and specific requirements for full-stack web integration with your AI projects. With expertise in API development and efficient model optimization, I can seamlessly align with your expectations. Utilizing tools and frameworks such as TensorFlow or PyTorch in Python, I will ensure that your AI models are both robust and efficient. Collaboration is key, so a solid workflow using Git will ensure smooth integration and management throughout the development cycle. My skill set, which includes both Python and experience with languages like Ruby, Ruby on Rails, and Rust, positions me well to contribute flexibly across the stack. Let's work together to transform your green-field products into innovative, scalable solutions. Best Regards, Khorshed Alam, RS Software

@ricardo686

Most training pipelines get built to work once on a laptop, then someone tries to scale it onto real GPU infrastructure and discovers the whole thing needs rewriting for distributed training, checkpointing, and cost control. I build with production and cost efficiency in mind from the first script, not as a later optimization pass. I work regularly with PyTorch-based training pipelines on AWS GPU infrastructure, focusing on reproducibility and cost-aware optimization, mixed precision training, gradient accumulation, and smart checkpointing to avoid wasting compute on failed runs. For this role, I'd build the pipeline end-to-end, data ingestion through training loop through evaluation against your baseline benchmarks, with versioning and monitoring baked in so models moving to production have a clear audit trail. I'd also make sure the prediction service exposes cleanly enough that your Rust and Rails teams can integrate it without friction. I can start right now Regards

@starttechno

Owning the full training workflow—reproducible Python pipelines, tight training loops, and wringing throughput out of AWS GPU clusters—is exactly my lane. Recently I fine-tuned a transformer classifier on ~1.2M labeled records; mixed-precision plus gradient accumulation cut epoch time ~40% and lifted F1 from a 0.81 baseline to 0.89. My favourite optimisation is mixed-precision (AMP) paired with an LR warmup+cosine schedule—it lets you fit larger batches, train faster, and usually improves stability without hurting accuracy, which directly hits your speed/cost/accuracy targets. On AWS I lean on EC2 g5/p4 instances with SageMaker for managed training and hyperparameter tuning, S3 for versioned datasets, ECR for reproducible containers, and CloudWatch for post-deploy drift monitoring. I'd version data and models with DVC + MLflow, add unit tests around the data transforms and training step, document the whole run, and expose predictions through a FastAPI service your Rust and Rails back-ends can call cleanly. To scope it right: what does your current baseline benchmark actually measure, and for the customer-facing features is inference latency or throughput the harder constraint? Muhammad Saad

@toriquldev123

Hello There! I’m Md Toriqul Islam, and I’m excited to partner with you. I can dive into your project immediately. I have strong experience in Python development, AI integration, backend systems, and cloud-based application development. I understand you're looking for an engineer to build reproducible ML pipelines, optimize model training on AWS, and collaborate with full-stack teams to deliver production-ready AI services. I can develop scalable Python-based training workflows, integrate model versioning and monitoring, and expose prediction APIs that fit seamlessly into your backend architecture. I am skilled in Python, PyTorch, TensorFlow, FastAPI, AWS, Docker, REST APIs, Git, and MLOps. Recent ML Project: I have worked on AI-powered applications involving model integration and Python-based data processing. I’d be happy to discuss the most relevant project experience that aligns with your technical requirements during our conversation. Favorite Optimization Technique: Mixed Precision Training (FP16), as it can significantly reduce GPU memory usage and improve training speed on supported hardware. AWS Experience: EC2, S3, SageMaker, IAM, CloudWatch, and Docker-based deployment workflows for machine learning applications. I’m ready to start immediately and would be happy to discuss your benchmarks, architecture, and project roadmap. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@teodorgeorge

Hello, My recent work includes Python Machine Learning (ML) pipelines on AWS, with data preparation, training loops, and model versioning. One project used 1.8M rows, improved F1 from 0.74 to 0.86, and used mixed precision training to reduce GPU cost. I will set up a reproducible PyTorch or TensorFlow pipeline, add unit tests, document the workflow, and compare results against your baseline. The service can be prepared for prediction APIs used by your Rust micro-services and Ruby on Rails back end. Best regards, Teo

@ska234

Hi. To build this out, I’d set up a reproducible Python ML training pipeline with PyTorch, data validation, experiment tracking, and automated evaluation so your team can ship models with confidence. I’d structure the workflow for AWS GPU training, then tune the input pipeline, batch strategy, mixed precision, and checkpointing to cut cost while improving throughput and accuracy. For production readiness, I’d package the model behind a clean API path that fits your Rust micro-services and Rails backends, with versioned artifacts and monitoring hooks. My focus is on getting a benchmarked training system running end to end, not just a notebook prototype. As a Senior AI Engineer, I have mastered Python, PyTorch, AWS ML stacks, model optimization, and production deployment workflows, and have strong experience in end-to-end training systems, cloud GPU pipelines, and API-driven AI products. In a recent project, I trained a computer vision model on 1.2M images and improved top-1 accuracy by 7.4% after pipeline and hyperparameter optimization. My favorite optimization technique is mixed-precision training with efficient data loading, because it lowers GPU cost while increasing training speed without hurting quality. I am sure I can deliver high-quality results within the right timeline based on project size. Let’s get in touch and discuss more. Thanks.

@sekai1116

With an extensive background in full-stack, mobile, and AI engineering, I believe I’m the perfect fit for your project. Over the years, I have been immersed in the design and delivery of modern web applications, cross-platform mobile apps, and AI-powered solutions. This has seen me go through end-to-end software delivery processes which aligns well with your need for a machine learning expert who appreciates such frameworks. My experience also extends to optimizing training workflows and fine-tuning models for superior effectiveness and accuracy which will be quite handy in your project. In recent times, my focus has been heavily set on AI development because I understand the power it wields in revolutionizing industries. I've specifically worked with technologies like OpenAI’s GPT, Claude, Gemini API integration among others that require solid python skills like those that you demand. Lastly but importantly for your project concerns is my versatile skills in cloud and DevOps. Over time using AWS services including GPU intensive clusters for ML workloads is one thing I have thrived at. As such, leveraging our partnership won't just get you top-notch algorithmic results from me but also a seamless, scalable infrastructure to hold them when deployed right from AWS where your ecosystem looks to be rooted. To say that I am thrilled by this opportunity would be an understatement;i cannot wait to help push your AI projects—and the entire platform—to the next level!

@alizain721

Hi, I will build and optimise your end to end Python training pipeline and get it running efficiently on your AWS GPU clusters. I trained a PyTorch recommendation model on 50 million events and improved recall@20 by 18 percent versus the provided baseline. I design reproducible pipelines with clear documentation and unit tests, implement efficient training loops and checkpointing, and add model versioning and runtime monitoring so production regressions are visible. My favourite optimisation technique is mixed precision training because it increases throughput and reduces memory use, enabling larger effective batch sizes and faster convergence without hurting accuracy. For AWS I routinely use S3 for data, EC2 Spot GPU instances and ECR for containers, SageMaker training and Model Monitor, plus CloudWatch for logs and alerts. Happy to jump on a quick chat. Do you prefer PyTorch or TensorFlow for the initial pipeline? Ali Zain

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ Hi, Thank you for checking my proposal and sharing the detailed project brief. I can build your machine learning training pipelines using Python, incorporating premium, efficient design principles. I will deliver: • Reproducible ML pipelines tailored for AWS using PyTorch or TensorFlow. • Optimized training workflows for enhanced speed, cost-efficiency, and accuracy. • Continuous model evaluation, versioning, and monitoring post-deployment. • Robust APIs to expose prediction services, collaborating with cloud and full-stack teams. • Comprehensive documentation and unit tests to guarantee quality. You will also receive: • A performance comparison against baseline benchmarks. I am confident I can execute your vision professionally and efficiently. Looking forward to discussing timeline and next steps. Best regards, Chirag Pipal

@demivision

Hi there, Employer, We’re Demivision LLC, a team passionate about building robust AI-driven platforms and delivering scalable, production-ready ML solutions. Your vision for blending cutting-edge machine learning with full-stack web capabilities resonates strongly with our expertise. We understand your immediate priority: developing high-performance, reproducible ML pipelines in Python, optimising model training, and ensuring these models integrate seamlessly with Rust microservices and Ruby on Rails APIs. Our team has extensive experience designing end-to-end training workflows—crafting efficient data pipelines, implementing advanced training loops (primarily with PyTorch), and maximising GPU utilisation on AWS for both speed and cost-effectiveness. For example, in a recent project, we developed a customer intent prediction model using over 2 million text records. Our pipeline leveraged AWS S3 for data storage, EC2 and SageMaker for distributed training, and Lambda for dynamic model deployment. Through careful hyperparameter tuning and mixed-precision training, we improved baseline F1 scores from 0.68 to 0.82. Our favourite optimisation technique is gradient accumulation combined with mixed-precision training. This approach significantly reduces memory usage and training time, allowing us to scale models efficiently without sacrificing accuracy—crucial for large datasets and cost-sensitive cloud environments. We are well-versed in AWS services for ML workloads, including SageMaker, EC2 GPU instances, ECR, Lambda, and CloudWatch for monitoring and versioning. We prioritise clear documentation, robust unit tests, and close collaboration with full-stack teams to ensure our models power reliable, customer-facing features. We’re excited to partner with you to push your AI initiatives and platform capabilities to new heights. Let’s discuss how Demivision LLC can deliver immediate value as you expand your engineering group.