Part-Time AI/ML Engineer Needed

@corpmember29

HELLO, I HAVE 10+ YEARS OF EXPERIENCE IN END-TO-END MACHINE LEARNING, NLP, COMPUTER VISION, AND PREDICTIVE ANALYTICS, AND CAN SHARE RELEVANT PROJECT EXAMPLES. I understand you need a reliable engineer to own model development across text, image, and tabular data. I can prepare and augment raw datasets, experiment with architectures (Hugging Face Transformers, CNNs in PyTorch/TensorFlow, XGBoost/scikit-learn), perform hyperparameter tuning, and deliver production-ready models in Docker or serverless environments with monitoring hooks. My focus is on reproducible, well-commented pipelines and clear technical documentation. APPROACH → initial sprint: audit raw data → define preprocessing pipelines → prototype multiple architectures → evaluate and select models outperforming heuristics → package winning models into production-grade services → integrate monitoring and logging → maintain private Git repo with concise notes. I WILL PROVIDE 2 YEAR FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE, WORK WITH AGILE METHODOLOGY, AND ASSIST FROM INITIAL MODEL DESIGN TO DEPLOYMENT AND MONITORING. I am ready to start immediately, take full ownership, and commit consistent weekly hours to deliver high-quality, production-ready machine-learning models. I eagerly await your positive response. Thanks

@nrajuu07

As an AI/ML engineer with a decade of experience in both Computer Vision and Machine Learning, I'm confident that I have the right mix of skills to meet and exceed your project's requirements. From tokenizing text with Hugging Face Transformers to refining CNNs in PyTorch and TensorFlow, I've had hands-on experience with the full spectrum of tasks you outlined. Moreover, my understanding of Natural Language Processing, Predictive Analytics and neural networks isn't just theoretical; it has been refined and sharpened through multiple successful projects. Lastly, my past work on machine-learning models that outperformed heuristic baselines makes me particularly excited about your project. From data preparation to model packaging, I regularly manage end-to-end processes using Docker or serverless frameworks. So if you're seeking an AI/ML engineer who's not only knowledgeable but also diligent when it comes to delivering high-quality work with a strong ownership mindset, I think we will make a great team!

@Ekarthaan

Hi Arthur S., This is quite similar to a project I delivered last week, so I can jump straight into execution. Ready to start immediately. Which KPIs and latency/cost targets should Sprint 1 optimize across NLP/CV/tabular, and what heuristic baselines must we beat? What production target are we deploying to (Docker on k8s vs serverless), with available CPU/GPU, expected QPS/latency SLOs, and any data-privacy constraints? Standardize experimentation early: repo scaffold with Poetry, pre-commit, Hydra configs, MLflow tracking, and DVC data/versioning to ensure reproducibility and lineage. Ship as composable services via FastAPI+BentoML, with ONNX/quantization or distillation for low-latency inference, plus Prometheus/OpenTelemetry hooks for drift and performance monitoring. Action Plan: Phase 0 (2–3d) Discovery/Setup—confirm KPIs, ingest data, stand up repo/CI (GitHub Actions), MLflow/DVC, Makefile for single-command runs. Phase 1 (3–5d) Baselines—clean splits; HF text model; ResNet transfer; XGBoost; log metrics. Phase 2 (1–2w) Data & Tuning—robust augmentation (NLP/CV), feature engineering, Optuna sweeps, k-fold CV. Phase 3 (3–5d) Package & Deploy—containerize winners, /predict API, load tests, one-command deploy; monitors/alerts. Phase 4 Handoff—concise notes, reproducible scripts, next-sprint backlog. Best Regards, Sid

@abhisaini0188

Hello, As the potential fit for this project, I want to focus on my dedication to results-driven development and eight years of well-rounded experience as a Full Stack Developer. What sets me apart are my deep skills in Computer Vision and Machine Learning and my ability to create intelligent, AI-powered systems that have a real business impact. Over the past few years, as the demand for AI-powered applications grew, I've built a diverse portfolio which includes developing & integrating Machine Learning models such as OCR, NLP, and much more- definitely in line with your requirements. Excelling in producing clean code within well-commented environments using Git is another aspect where you'll find me adept. Even beyond that, I understand the essence of technical notes and reproducible training pipelines in ensuring system continuity and thereby adding value to your project. Moreover, having a strong hold on backend systems (Python, Node.js, Laravel) means that I can provide the end-to-end treatment your project needs without any hitches. My time management skills would ensure consistent support making me an ideal candidate for this project. Excited about your first sprint! Let's make this tech marvel a reality! With Regards!

@Rekhathakur

Dear Client, I'm Rekha Thakur, an experienced Full Stack AI/Machine Learning engineer with deep expertise in a range of NLP tasks and Python. My projects mirror the complexity and amplitude demanded by yours, featuring multi-step reasoning agents (LangChain, LangGraph, GPT-4) and ML pipelines. Over the years, I've refined my skills to find creative solutions for problems—be it tokenising text with Hugging Face Transformers or enhancing CNNs using PyTorch or TensorFlow. My robust stack is equipped with the tools you need: `OpenAI`, `Hugging Face`, `Claude`, `TensorFlow`, `PyTorch`. Moreover, I don't limit myself to purely technical boundaries. To offer faster ROI, I blend AI with full-stack development and automation. This approach will translate into an end-to-end, timely-delivered project for you - from data preparation to experimenting, training/hyper-tuning models and Docker/serverless packaging ratified with monitoring hooks. I promise you clean and well-commented code in a private Git repo ensuring absolute reproducibility. In addition to this, concise technical notes will be provided to facilitate your understanding of how the project advanced. My past clients have consistently praised my clear communication which fosters a greater sense of trust and alignment between us. So, let's get started on achieving great heights by outperforming heuristic baselines with brand new legendary ML models!!! Thank you!!!!

@alizain721

I know how frustrating it is when models beat a baseline in notebooks but never survive the handoff to production. I build and ship experiments that win and deploy with one command. The best thing about me is I’ve worked on a very similar project recently: for Skallix I built end-to-end ML pipelines combining Hugging Face tokenizers for text signals, XGBoost and scikit-learn for tabular forecasts, and PyTorch CNNs for image tasks, all Dockerized with CI, training logs, and simple monitoring hooks in a private repo. I understand the flow you need: raw data ingestion and augmentation, iterative experiments and hyperparameter tuning, reproducible training pipelines, and packaging models as Docker or serverless services with monitoring. I’d use Python, Hugging Face, PyTorch/TensorFlow, XGBoost, MLflow/GitHub Actions, and Docker or AWS Lambda/ECS depending on scale. Quick questions: what is the typical dataset size and label quality for the first sprint, and do you have a preferred cloud or CI for deployments? Happy to chat or jump on a short call to plan sprint one. Regards Ali Zain!!

@sf264

Hello, I am very interested in taking ownership of developing brand new machine learning models while working on a flexible, part time schedule. I will commit to a consistent number of hours per week. Please message me to discuss more details. Excited to collaborate with you, Fahad.

@asharib17

I’ve built end-to-end ML pipelines across NLP, CV, and tabular models—taking projects from raw data to production-ready services with reproducible training and clean deployment. Approach (Sprint 1): * Data audit + preprocessing pipeline (validation, cleaning, augmentation) * Establish strong baselines (TF-IDF/LightGBM, simple CNN, etc.) * Define metrics + evaluation framework * Train initial models (Transformers for NLP, CNNs for CV, XGBoost for tabular) * Hyperparameter tuning (Optuna/early stopping) * Select best-performing model vs baseline Production Setup: * Package model as API (FastAPI) * Dockerized deployment (single-command run) * Logging + monitoring hooks (latency, accuracy drift basics) * Versioned experiments + reproducible pipelines Stack: PyTorch / TensorFlow, Hugging Face, scikit-learn, XGBoost, Optuna, FastAPI, Docker Deliverables: * Clean, well-structured repo * Reproducible training pipeline * Trained model outperforming baseline * Deployment-ready service * Concise technical notes I focus on practical ML—measurable improvements, clean pipelines, and deployable systems from day one.

@zahidhasta

Hello, I will take end-to-end ownership of building new ML models across NLP, CV, and predictive analytics, delivering production-ready solutions with clean code and reproducible pipelines. My approach includes thorough data preparation, rigorous experimentation, and deployment via Docker or serverless architectures. I can commit consistent weekly hours and am ready to discuss your first sprint priorities. Best regards, Zahid Hassan

@emilesd617

Hi, I am a full-stack AI/ML engineer with 8 years of rich experience with a background in Python, PyTorch, TensorFlow, Hugging Face, and predictive analytics. For this project, the most important part is building strong data pipelines and training workflows that consistently produce models better than baseline while staying easy to deploy. I can handle data preparation, model development, tuning, and package everything into clean, reproducible services with Docker and monitoring-ready structure. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@krisk63

Hi there, I'm Kristopher Kramer from McKinney, Texas. I’ve worked on similar projects before, and as a senior full-stack and AI engineer, I have the proven experience needed to deliver this successfully, so I have strong experience in Finite Element Analysis, CAD/CAM, Computer Vision, Python, Predictive Analytics, Machine Learning (ML), Natural Language Processing and Data Augmentation. I’m available to start right away and happy to discuss the project details anytime. Looking forward to speaking with you soon. Best regards, Kristopher Kramer

@altaireon

This role aligns well with my experience and working style—I enjoy end-to-end ownership across NLP, Computer Vision, and predictive modeling. I bring 8+ years of industry experience building and deploying ML systems. I’ve worked with Transformers (Hugging Face) for NLP, CNNs in PyTorch, and tabular models using scikit-learn/XGBoost, covering the full pipeline from data preparation and augmentation to model training, tuning, and production deployment. I’m comfortable packaging models into production-ready services (Docker/K8s) with reproducible pipelines, clean code, and monitoring—ensuring models can be deployed reliably with minimal effort. I can independently drive development, iterate quickly, and deliver maintainable, high-quality solutions while committing consistently on a part-time basis. For the first sprint, I’d focus on understanding the data, establishing baselines, and setting up a reproducible training pipeline.

@hassam945

Hi this opportunity really matches how I like to work. I have been working in AI/ML for a few years now, and I’m comfortable across NLP, Computer Vision, and predictive models. I have used Hugging Face for text tasks, built CNN-based models in PyTorch/TensorFlow, and handled tabular problems with scikit-learn and XGBoost. I enjoy taking things from raw data all the way to a working, deployable model. For the first sprint, I’d start by understanding and preparing the data properly cleaning, augmenting, and making it model-ready. Then I’d build quick baseline models to set a benchmark, and from there move into improving performance with better architectures and tuning. Once we have a strong model, I’d package it into a simple, reproducible deployment setup (Docker or serverless), with basic monitoring in place. I keep my code clean, well-documented, and easy to reproduce so everything can run smoothly later. "I am comfortable working part-time with consistent hours and taking full ownership. Let’s discuss your goals and plan the first sprint together. I prefer to discuss first because we both need to understand first each other then if its align we start working. Last thing , I am not here for juggling clients you visit my freelancer profile it gives you clarity about all things you need .I am confident about that thing I deliver your work with requirement satisfaction and Clarity."

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Also, we are a team of professional workers, and we are always available 24/7 to help employers without limitations, and delivery is guaranteed on time. Your faithfully. Eng. Alema Akter

@MIndlabsAi6

Hello, I'm Vishal Maharaj, with 20 years of expertise in Python, Computer Vision, and Data Augmentation. I have carefully reviewed your project requirements. To ensure success, I propose a meticulous approach. Firstly, I will meticulously preprocess and augment raw data to enhance model readiness. Next, I will innovate, train, and fine-tune cutting-edge architectures tailored to your needs. Finally, I will deploy these winning models as production-grade services, incorporating monitoring features for seamless operations. I am committed to delivering clean, well-commented code in a private Git repo, along with detailed technical documentation and reproducible training pipelines. If you're interested in discussing this further, please initiate a chat. Cheers, Vishal Maharaj

@engrhasan01

I’m AI and ML/DL engineer with 5+ years of experience in AI/ML, data processing, and model deployment, working across NLP, Computer Vision, and predictive analytics. I focus on building production-ready models from scratch that are accurate, scalable, and easy to deploy. How I’ll Approach Your First Sprint: • Data Preparation: Clean, label, and augment datasets for NLP (tokenization via Hugging Face), CV (image pipelines), and tabular models • Model Development: Train and tune models using PyTorch/TensorFlow, with structured experiments and performance tracking • Optimization: Hyperparameter tuning and benchmarking against baselines to ensure measurable improvement • Deployment: Package models into Dockerized or serverless services with reproducible pipelines and monitoring hooks Deliverables: • Clean, well-documented code in Git • Reproducible training pipelines • Deployed model endpoint (one-command setup) • Concise technical notes I can commit consistent weekly hours and take full ownership from data to deployment. My goal is to deliver models that are not just functional, but production-grade and scalable.

@invoketech

Hello, I have reviewed your requirement—you need a part-time AI/ML engineer who can take full ownership of building, training, and deploying high-performance models across NLP, Computer Vision, and predictive analytics. With 10+ years of experience in AI/ML engineering, I’ve developed end-to-end pipelines using PyTorch, TensorFlow, Hugging Face, and scikit-learn, delivering production-ready models with strong performance improvements over baseline systems. <<------what I’ll deliver--------->> • End-to-end ML pipelines (data prep → training → deployment) • NLP models using Transformers (tokenization, fine-tuning) • Computer vision models (CNNs, optimization & evaluation) • Predictive models (XGBoost, sklearn with feature engineering) • Dockerized / serverless deployment with monitoring hooks • Clean, reproducible code + documentation in private repo <<----proposed approach----->> I’ll begin with data analysis and preprocessing pipelines, then experiment with multiple model architectures and hyperparameter tuning to outperform baselines. Once validated, I’ll package models into scalable, production-ready services with monitoring and reproducibility in mind. I am ready to commit consistently and eagerly waiting for your response to get started . INVOKE TECH

@Hrolgar

I build and deploy ML models across NLP, computer vision, and predictive analytics — not as academic exercises but as production systems that run autonomously. What I bring: - NLP: built text classification and entity extraction pipelines using Hugging Face Transformers, fine-tuned on domain-specific data - Computer Vision: image classification and object detection with PyTorch, OpenCV preprocessing, model export to ONNX for deployment - Predictive Analytics: scikit-learn, XGBoost, feature engineering on messy tabular data, proper cross-validation and metric selection - Full pipeline ownership: data prep, training, evaluation, packaging, deployment, monitoring I currently run an AI agent platform in production that orchestrates multiple ML-powered workflows autonomously — so I understand the full lifecycle from raw data to deployed model to maintenance. Available part-time on a flexible schedule. I work async and deliver consistently. Happy to start with a small task so you can evaluate my work before committing to ongoing collaboration.

@mahads31

As a Software Developer with an extensive expertise in various Python libraries, I believe I am the perfect fit for your AI/ML project requiring a solid command over Natural Language Processing, Computer Vision, and Predictive Analytics. My experience with Hugging Face Transformers, PyTorch, TensorFlow, scikit-learn and XGBoost will definitely play a vital role in preparing and augmenting your raw data, experimenting with fresh architectures, and finally packaging the models into production-grade services. Throughout my career, I've consistently showcased my commitment to building clean code in a private Git repo along with providing concise technical notes plus reproducible training pipelines--all essential elements that you require as acceptance criteria. I'm also well-versed with Docker and serverless technologies for efficient deployment purposes, making sure that models are easily accessible for real-time monitoring. In addition to my technical competencies, I bring an invaluable sense of ownership to my work. Your quest for novel models and an inclination for outperforming heuristic baselines resonate soundly with me. I not only embrace end-to-end ownership but am driven by it. So if appointed, you can count on me not just to complete tasks but lead the development of cutting-edge AI/ML solutions that accurately cater your business needs. Let's hop on a call and discuss how I can specifically approach the first sprint based on your project’s specifications!