Part-Time Full Stack AI/ML Engineer -- 2

@gunslinger

Hey, this sounds right up my alley. I’ve been building end to end systems for years, from messy raw data all the way to production-ready services, mostly in python based stacks. I’m comfortable with ETL pipelines, model experimentation (including RL workflows), and wrapping everything into scalable APIs with Docker/K8s on AWS. I’ve worked with US clients so communication and ownership won’t be an issue, and I’m used to weekly check-ins and moving fast without breaking things. I can help you get a solid, reproducible pipeline plus a clean deployment that actually works in production, not just in notebooks. Let me know what your current setup looks like and where you want to start. Kindly contact me for further discussion.

@LiveExperts

Hello, Building a product that relies on cutting-edge reinforcement learning is no small feat, but it's exactly the kind of challenge my team at Live Experts thrives on. As an experienced engineer with a strong background in machine learning, deep learning, and artificial intelligence, we have perfected our skills in using tools like PyTorch and TensorFlow to develop models that meet and exceed performance benchmarks. From data preprocessing to model development, we can take your idea from raw data all the way to a production-ready service. Additionally, our expertise in popular RL libraries such as Stable-Baselines3 and Ray RLlib coupled with fluency in Python (pandas, NumPy) gives us an edge in tackling this project's unique challenges. Furthering this, I also have relevant experience with CI/CD (GitHub Actions), infrastructure-as-code (Docker/Kubernetes), and basic DevOps which would help ensure seamless integration with your existing Python micro-services stack on AWS. Our professionalism extends beyond technical skills - as an organization that takes ownership seriously, we are committed to regular check-ins and efficient collaboration. I am confident in being able to deliver your expectations such as reproducible training pipelines with documented code, a baseline RL model that meets your performance standards, REST/GraphQL endpoints as services pluggable into your system and a deployment guide accompanied by key findings f Thanks!

@Softeria

✅ Proposal for Part-Time Full Stack AI/ML Engineer With a strong background in Python, reinforcement learning, and full-stack development, I am ideally suited to bring your AI product from concept to production. My experience includes designing ETL pipelines, implementing RL algorithms using PyTorch and TensorFlow, and integrating models into existing systems on AWS. Proficient in Docker, Kubernetes, and CI/CD practices, I ensure smooth, scalable deployments. I commit to delivering a reproducible training pipeline, a performance-tuned RL model, and a robust API that integrates seamlessly into your service architecture. I look forward to driving impactful results through regular collaboration and innovative problem-solving. Let’s advance your project together.

@Mahihassi

Having worked on similar projects in the past, my team at MHTechFusion brings the combination of expertise your project needs. With our skills and experience, we provide efficient full-stack solutions from the initial data preprocessing to model development, through to integration with existing systems. We are Python-first in everything we do and are proficient with PyTorch, TensorFlow, NumPy, pandas, as well as popular RL libraries like Stable-Baselines3 and Ray RLlib - precisely aligning with your project requirements. Moreover, our strong grasp on CI/CD (GitHub Actions) and infrastructure-as-code coupled with DevOps practices ensures seamless collaboration and timely progression of the project. Our core expertise in real-time dashboards and data visualization also guarantees a robust API or service exposing inference endpoints that will integrate smoothly into your existing system. In terms of deliverables, our commitment to producing reproducible code with thorough documentation aligns perfectly with your expectations. Additionally, we cater to your need for a performance-optimized LLM model capable of reaching the agreed-upon benchmark. Furthermore, our deployment pipelines along with key findings from experiments will be provided in concise deployment guides. When you hire us, you're effectively embracing the strength of a team that takes ownership seriously and is genuinely committed to transforming your concept into a top-notch production-ready service.

@Herculesz

Hi Taking an RL idea to production usually fails at the transition from experimentation to stable, reproducible pipelines and deployable services. I’ve built end-to-end Python systems combining ETL pipelines, RL model training (PyTorch, Stable-Baselines3, RLlib), and production APIs on AWS. For your case, I’d structure a reproducible data pipeline with validation, then iterate on RL models with clear reward design, experiment tracking, and benchmark-driven tuning. The key challenge is ensuring training consistency and avoiding environment drift, which I handle using containerized workflows, versioned datasets, and controlled experiment configs. I’d then expose the trained model through REST or GraphQL endpoints, containerized with Docker and ready for Kubernetes deployment. Integration with your existing microservices would be seamless, with CI/CD pipelines ensuring stable releases and rollback capability. You’ll get clean, documented code, reproducible training, and a production-ready inference service aligned with your benchmarks. Thanks, Hercules

@AITSoft

Hello, I understand you need a hands-on engineer to take your reinforcement learning project from raw data to a production-ready service. I can build strong ETL pipelines to clean and prepare your data, then develop and tune reinforcement learning models using libraries like PyTorch and Stable-Baselines3. I’ll wrap the model as an API with Docker containers, integrating it smoothly into your existing Python micro-services on AWS. I'll focus on producing clear, documented code and quick iteration to meet your performance goals and deployment needs. Regular weekly check-ins and end-to-end responsibility will be a priority. What are the key performance benchmarks you want the RL model to achieve? Is there a preferred structure or format for your ETL pipelines and data validation? Can you share more details about your current Python micro-services stack and API requirements? Are there existing CI/CD or DevOps tools you want to integrate with? What is your expected timeline for initial milestones? What key performance benchmarks are you targeting for the reinforcement learning model? Thanks,

@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, Machine Learning (ML), Big Data Sales, Hadoop, Map Reduce, Docker, Data Analysis, ETL, Reinforcement Learning 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.     

@juanjosec63

Hi, As a individual developer I’m available to start right away. I can help in your project focusing on building an end-to-end reinforcement learning pipeline, including data preprocessing and ETL workflows, model development and tuning, experiment tracking, API integration, containerization, and all related ML and backend modules to fix, improve, and develop during the project. With my expertise in full-stack and machine learning development and experience working with modern technologies like Python, PyTorch, TensorFlow, Stable-Baselines3, Ray RLlib, Pandas, NumPy, Docker, Kubernetes, AWS, REST APIs, and CI/CD pipelines, I can deliver a reproducible training system, optimized RL model, and production-ready inference service. You can expect clear communication, fast iteration through milestones, and a high-quality result that fits seamlessly into your existing workflow. Best regards, Juan

@nrajuu07

As an AI/ML Engineer with deep expertise in Python, TensorFlow, PyTorch, and popular RL libraries like Stable-Baselines3 and Ray RLlib, I am confident in my ability to drive your project towards success. My skillset aligns perfectly with your job description; from setting up robust ETL pipelines to researching the most effective reinforcement learning algorithms, my experience will allow me to take your raw data and transform it into a precise and performance-driven production-ready model. My hands-on approach combined with my meticulous attention to detail will ensure I fulfill your project needs, while providing a reproducible training pipeline and documented codebase for seamless future maintenance. You can count on me to not only hit but exceed the performance benchmarks set by you and deliver a proficient API/service that integrates flawlessly into your existing system. Additionally, my familiarity with CI/CD through platforms like GitHub Actions further enhances my suitability for this role as I can ensure a streamlined development process. With a proven record of delivering on time without compromising on quality, I promise to provide not just what you need in each milestone, but also insightful experiment findings that can provide further optimization opportunities. Let's work together to bring your cutting-edge reinforcement learning dream into reality!

@Solutionzhere

Hi, This is very much in our lane at SolutionzHere. We can take an RL project from data prep to a production service, with Python-first delivery, reproducible training, model evaluation, Docker/Kubernetes packaging and AWS-ready integration. For this scope, I’d suggest $20–30/hour for a strong hands-on RL engineer and a realistic 4–8 week delivery path for the first production-grade milestone set, depending on data quality and how mature the success metric is. If the benchmark is still being defined, I’d tighten scope first so the work stays measurable. We’re comfortable with weekly check-ins and milestone-based iteration. One key question: is the first use case decision-making/recommendation, control/optimization, or agent training in a simulated environment?

@jushuamurillo98

Hello there, I’ve carefully reviewed your project and am excited about the opportunity to work with you. With over six years of experience in full stack machine learning engineering, I specialize in developing end to end reinforcement learning systems that scale reliably in production. I am confident I can transform your raw data into a production ready RL service while maintaining strong ownership and smooth collaboration. Here’s my approach: Build clean and automated ETL pipelines to process structured and unstructured data efficiently. Develop and benchmark RL models using PyTorch and Stable Baselines while iterating quickly on experiments and integration. I am available to start immediately and aim to deliver the initial training pipeline and baseline model within seven days. Additional instructions or notes optional: I ensure seamless integration with AWS microservices and containerised deployments. I maintain clear documentation and reproducible workflows for long term maintainability. Best regards, Jushua

@quan9824

As an enthusiastic and seasoned Full-Stack AI/ML Engineer, I’ve had the opportunity to work on diverse, cutting-edge projects that align closely with your needs. Proficient in Python, PyTorch and TensorFlow, I am fully equipped to handle stimulating tasks such as robust ETL pipelines and transforming heterogeneous data into clean forms. My diverse experience includes designing intuitive APIs backed with sophisticated AI models connecting multiple services - something surely useful in pluging my work into your Python microservice stack on AWS. While my skill set is broad in reinforcment learning (RL), ranging from Stable-Baselines3 to Ray RLlib, I especially wish to emphasize my ability to backup decisions with comprehensive research and tune hyperparameters efficiently. Documented code is a crucial part of the work and you can rest assured knowing I prioritize thoroughness when it comes to insights into my work- I will lay out precise summaries of my findings during experiments in the form of a deployment guide. To conclude, my hands-on experience in real-world problems that are effectively addressed through RL combined with my commitment to ownership make me your ideal choice for this project. If you share my passion for end-to-end responsibility in AI application development as well as iterative problem-solving, I look forward to discussing the details further with you.

@abhisaini0188

Hi, I can take full ownership of your end-to-end RL pipeline, from data preprocessing to production deployment. Approach: Data Layer: Robust ETL pipelines with validation (pandas, NumPy) RL Development: PyTorch + Stable-Baselines3/RLlib for fast experimentation, tuning, and evaluation Deployment: Containerized APIs (FastAPI) integrated into your Python microservices on AWS MLOps: Reproducible training pipelines + CI/CD (GitHub Actions) Deliverables: Documented, reproducible training pipeline Baseline RL model meeting target benchmarks Scalable inference API (REST/GraphQL) Deployment guide + experiment insights I have experience building ML pipelines and production-ready services, ensuring both research flexibility and system reliability. Available for regular check-ins and iterative milestones. With Regards!

@maximg10

⭐Hey, I’m ready to assist you right away!⭐ I believe I'd be a great fit for your project since my expertise aligns perfectly with your requirements. With a commitment to ownership and regular check-ins, I can ensure seamless collaboration. My hands-on experience in data preprocessing, model development, and system integration encompasses Python, PyTorch, TensorFlow, pandas, NumPy, and advanced RL libraries like Stable-Baselines3. I excel in designing robust ETL pipelines, tuning hyperparameters, and deploying models behind REST/GraphQL endpoints.

@hotanloc

With deep involvement in end-to-end AI/ML project management, I am well-suited to carry out the data preprocessing and model development tasks. I have succeeded in designing robust ETL pipelines, effectively cleaning and transforming complex datasets, and automating data validation for previous clients. My fluency with Python, along with reinforcement libraries such as Stable-Baselines3 and Ray RLlib ensures that I can swiftly implement the necessary reinforcement learning algorithms for your project. Furthermore, my expertise extends into system integration and full-stack web development, making me the ideal candidate to containerize your trained models using Docker/Kubernetes and integrate them within your current Python micro-services stack on AWS. Not only will I deliver a reproducible training pipeline but also a baseline RL model meeting the agreed-upon performance benchmark. Additionally, I will build an API or service exposing inference endpoints that seamlessly wedges into your existing structures.

@vishal1145

Your RL model will fail in production if the reward function doesn't account for edge cases in your real-world data distribution. I've seen teams spend 6 months training agents that work beautifully in simulation but collapse when deployed because they optimized for the wrong metrics. Before architecting the pipeline, I need clarity on two things: What's the action space complexity you're dealing with - are we talking discrete actions under 100 or continuous control with thousands of dimensions? And what's your current data volume - are we processing gigabytes daily or terabytes that require distributed training across multiple GPUs? Here's the architectural approach: - PYTORCH + RAY RLLIB: Build a distributed training pipeline with checkpointing and experiment tracking (Weights & Biases) so you can resume failed runs and compare 50+ hyperparameter configurations without manual logging. - ETL + PANDAS: Design idempotent data pipelines with schema validation using Great Expectations to catch data drift before it poisons your reward signal and causes model degradation. - DOCKER + AWS ECS: Containerize inference services with auto-scaling policies and health checks so your API handles traffic spikes without cold starts killing response times during peak usage. - STABLE-BASELINES3 + CUSTOM WRAPPERS: Implement curriculum learning and reward shaping techniques that I've used to reduce training time from weeks to 48 hours for similar RL problems. I've built 4 production RL systems including a dynamic pricing engine that increased revenue 23% and a resource allocation optimizer handling 50K decisions per second. I don't take on projects where the success metrics are vague. Let's schedule a 20-minute call to walk through your reward structure and failure scenarios before we commit to milestones.

@kareemmaize

Hi there To take a reinforcement learning idea from raw data to a production-ready service, the most critical part is building a clean, reproducible pipeline where data, training, and deployment all stay aligned. I’ll approach this by structuring the ETL and validation layer first, then developing RL models (Stable-Baselines3 / RLlib) with clear reward design and measurable benchmarks. From there, I’ll wrap the model into a containerized API service and integrate it into your existing Python microservices on AWS. This ensures experiments are repeatable, models are stable, and deployment doesn’t break when scaling. This means I understand your goal is not just experimentation, but a reliable system that moves from training → evaluation → production cleanly. My process is simple: build data pipelines and validation first develop and benchmark RL models iteratively deploy as API service with Docker and integrate into your stack I’m ready to start with data pipeline setup and baseline model training, then iterate based on results..

@maxscender

I can help you. The primary technical risk in moving Reinforcement Learning from research to production is "reward hacking," where the agent exploits gaps in the reward function rather than solving the actual problem. I will address this by implementing a rigorous reward-shaping validation phase and using Gymnasium wrappers to ensure environment state-spaces are perfectly aligned with your real-world data. To ensure your AWS-hosted micro-services handle RL inference efficiently, I will focus on optimizing the observation pipeline—the bottleneck is rarely the model itself, but the speed at which your system can pre-process and feed "state" to the agent. I will use Ray RLlib for distributed training to find stable hyperparameters quickly, then package the final policy into a lightweight TorchScript or ONNX runtime to minimize latency in your production Docker containers.

@arbu1499

Hi, I understand you need an end-to-end RL engineer to handle everything from data pipelines to deploying a production-ready service within your Python microservices stack on AWS. I’ve worked on similar workflows—building ETL pipelines, training/tuning models with PyTorch and RL libraries, and exposing them via REST APIs with Docker-based deployments and CI/CD. I can deliver a reproducible training setup, optimized model, and seamless integration with clear milestones and regular updates. Looking forward for your positive response in the chatbox. Best Regards, Arbaz N