Machine Learning / Data Engineer — English & Spanish Needed

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Robust ML Systems as a Machine Learning / Data Engineer ❇️ Hi My Friend, hope you are doing well. I reviewed your project requirements and see you are looking for a Machine Learning / Data Engineer. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in building data systems and ML models. I will create reliable data pipelines and production-ready models to meet your needs. ➡️ Why Me? I can easily handle your Machine Learning and Data Engineering tasks as I have 5 years of experience in Python, data pipelines, and machine learning models. My expertise includes data cleaning, SQL, and tools like PyTorch and TensorFlow. I also have a strong grip on cloud platforms and data orchestration tools, ensuring a smooth and efficient workflow for your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you examples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python ✅ pandas ✅ NumPy ✅ scikit-learn ✅ PyTorch ✅ TensorFlow ✅ SQL ✅ Airflow ✅ Docker ✅ Data Cleaning ✅ ML Model Deployment ✅ Cloud Platforms Waiting for your response! Best Regards, Zohaib Waiting for your Response!

@mak94pk

You need reliable ingestion, transformation, and model deployment from scratch. I’ll start by creating an Airflow DAG that pulls raw data, cleans it with pandas, and loads it into a PostgreSQL warehouse. From there I’ll prototype a scikit‑learn model, containerize it with Docker, and set up a CI/CD pipeline for production. I can deliver the end‑to‑end system in 30 days. What data sources and volume do you anticipate for the initial pipeline?

@OlektraGroup

Hi, I understand you need a scalable foundation for your data and ML systems, and I have the experience to build these from the ground up while communicating fluently in English and Spanish. I recently developed a license plate detection pipeline for traffic monitoring, where I handled raw data ingestion, cleaned noisy imagery, and deployed a production-ready model. For your infrastructure, I recommend using **Dagster** for orchestration because it provides superior visibility into data dependencies compared to standard cron-based scripts. On that project, I improved inference speed by 30% by converting models to **ONNX**, ensuring they ran efficiently on edge hardware. My full CV and GitHub portfolio are attached for your review. How are you currently handling your model versioning and data lineage as you scale?

@bcosley

Hi, We’re moving fast in data-heavy applications and multilingual teams often run into messy data and deployment gaps. I’ve worked on end-to-end pipelines for ingestion, cleaning, and serving models in production, with dashboards and monitoring to keep data quality visible across English and Spanish workflows. Execution: I’d start by validating current data flows, identify unstable stages, then implement tracing, lineage, and robust transformations. I’d fold in lightweight ML monitoring early, establish small, incremental model deployments, and validate results against business KPIs with simple AB checks. One technical risk: data quality drift from multiple sources could mask model signals if not caught early. Two clarification questions: - How do you balance latency vs accuracy for inference in production? - Which data sources are highest priority for initial pipeline coverage? If we're aligned, I’d be happy to walk you through the implementation plan before we get started. Best regards, Brandon

@PINESCRIPTERR

Hi, Building data and ML foundations for an early-stage startup requires practical choices: reliable ingestion, traceable transformations, reproducible training, and monitoring that reveals when data or model behaviour changes. I can work across Python, SQL, data pipelines, model development, containerisation, and production deployment. My approach separates raw ingestion, validated datasets, feature generation, training, inference, and monitoring so each stage can be tested and improved independently. I’d begin by translating the highest-value business problem into measurable acceptance criteria, then design the smallest maintainable pipeline that can support it. Documentation would cover architecture, assumptions, data lineage, model versions, deployment, and recovery procedures so knowledge does not remain with one developer. My CV and a detailed example covering the problem, contribution, tools, and outcome can be shared privately. I’m also comfortable collaborating through clear written updates and documented technical decisions. What is the first production use case this engineer would be expected to own: forecasting, classification, recommendations, or an LLM-based workflow? Regards, Houssame

@techplusintl

Hi, ★★★ Python SPECIALIST ★★★ I can build and maintain data ingestion and transformation pipelines to create reliable data and ML systems. I will focus on using Python with libraries like pandas and NumPy for data manipulation, ensuring that data is clean and organized. I will implement machine learning models using scikit-learn, PyTorch, or TensorFlow, depending on the specific requirements. To avoid issues with model deployment, I will set up monitoring systems to track performance and make adjustments as needed. I will use Airflow for orchestrating workflows, as it provides better control over data pipelines compared to other tools. I will also document all systems and processes to ensure clarity and facilitate future maintenance. To get started, I will need access to your existing data sources and any specific business requirements you have in mind. Thanks!

@TeraByte26

Hi Jessica, I will build and maintain data ingestion and transformation pipelines, develop and deploy production‑ready ML models, clean and organise messy data, document all processes, and collaborate fully in English and Spanish. I’ll deliver a complete system in six weeks for $1500. Ready to start now—does any specific data source need priority? Waiting for your response in chat! Best Regards.

@gotucat

Hi, I reviewed your request for a mid-level Machine Learning / Data Engineer to build data ingestion and transformation pipelines, then develop, deploy, and monitor ML models in production. I’ll use Python, NumPy, SQL to clean and organize messy data from multiple sources, implement reliable pipeline steps, and turn business needs into practical data and ML solutions. I’ll also handle model development and monitoring while keeping documentation clear for your team, with strong attention to English and Spanish collaboration. I focus on clean, maintainable systems, fast iterations, and dependable delivery as you scale from an early stage. Let’s discuss here now.

@muneeb6767

I have strong experience with Python, pandas, NumPy, scikit-learn, PyTorch, SQL, data pipelines, and AI/LLM integrations, building systems from data ingestion through production-ready ML workflows. I can contribute to cleaning complex datasets, model development, automation, and reliable deployment with clear documentation; I’m comfortable collaborating in English.

@vishal1145

Your ML pipeline will fail in production if you don't implement proper data validation and model drift monitoring from day one. Most startups skip this and spend months debugging silent failures. Quick questions - what's your expected data volume at 6 months, and are you planning real-time or batch inference? And what cloud platform are you leaning toward? Here is the architectural approach: - PYTHON + MLOPS: Build containerized training pipelines with automated retraining triggers when model performance degrades below threshold. - SQL + SPARK: Design partitioned data schemas that support incremental processing and prevent full table scans as your dataset grows. - AIRFLOW + DOCKER: Implement orchestrated ETL workflows with failure recovery and data lineage tracking for regulatory compliance. I've built similar zero-to-production ML systems for 2 startups that scaled from prototype to 100K daily predictions. Let's schedule a brief technical call to align on your data architecture before you commit to a stack.

@seefattechnologi

Hi, I’m a Machine Learning and Data Engineer with strong experience building reliable data pipelines and production-ready machine learning solutions using Python, pandas, NumPy, scikit-learn, PyTorch, SQL, and cloud technologies. What interests me most about your startup is the opportunity to build the data foundation correctly from the beginning. I can turn messy, fragmented data into dependable pipelines, develop models that solve real business problems, and take them through deployment and monitoring. I’m also comfortable working independently, documenting technical decisions, and communicating clearly in English and Spanish. I bring a practical mindset: build what is needed, keep systems maintainable, and measure the result. Example project: I built an automated data pipeline that collected and cleaned data from multiple sources, transformed it into analytics-ready datasets, and fed a machine learning model for prediction. I handled the pipeline, feature engineering, model development, deployment, and monitoring, resulting in a more reliable and automated workflow. Thanks!!!

@Aaleen110

The hardest part of this job is cleaning and organizing messy data from different sources, that is the hard part. I will build reliable data pipelines using Python and pandas for ingestion and transformation, I will also use SQL for structured data querying. For ML models, I will develop them with PyTorch, then deploy them using a containerized approach, likely Docker, and monitor them with tools like Prometheus and Grafana so they stay production-ready. The failure this job is most likely to hit is models not performing well in production due to data drift, so I will build in regular retraining pipelines and drift detection metrics. I am a Preferred Freelancer on Freelancer with a 5.0 rating, 100% on time and 100% on budget. What is the expected volume of data for the initial ingestion phase? I need the data source connection details to start.

@DrSyafiq

I can help you build this properly. You're building Machine Learning / Data Engineer — English & Spanish Needed, where the real delivery risk is usually in the workflow details, not just the feature list. Relevant fit: Python, SQL, Machine Learning (ML) with milestone-driven delivery. Key scope I would cover: Python implementation risks and acceptance criteria; SQL integration points and edge cases. If helpful, I can outline the first milestone around the riskiest part of the build before we start. Quick question: What part of this build has caused the most delivery risk so far: architecture, integrations, or product clarity? Also, if we align on the core scope first, would you prefer the first milestone to focus on a stable MVP foundation or on feature breadth? Best, Dr. Syafiq

@ianb65

Hi there, Your data pipelines and production ML stack need to be built cleanly from day one, not patched later. I’ve solved exactly this kind of machine learning / data engineering work before, and I can help turn messy inputs into reliable Spark and SQL workflows, then shape them into practical Machine Learning (ML) systems. I’ll organize ingestion, transformation, and model deployment steps, document the decisions clearly, and keep the process workable for both English and Spanish collaboration. Thanks, Ian

@AhmadXpert2410

Hi, I’m Muhammad Ahmad, an AI Engineer recently I worked with a UK-based telecom business on practical Data Science and AI solutions. Recently, I developed a Dormancy Prediction model to identify customers at risk of becoming dormant. The model achieved 80% precision on production data and supported proactive customer retention. I also built a RAG-based AI chatbot using Python, FastAPI, FAISS, LLMs, Docker, and AWS to retrieve relevant information and generate context-aware responses. In addition, I’ve worked on data processing, SQL/Python workflows, reporting, and automation, preparing business data for ML and AI applications. I’m interested in this opportunity because it combines Data Engineering, Machine Learning, and Generative AI to solve real business problems. I’d be happy to share more details about these projects and discuss thing further. Best regards, Muhammad Ahmad

@revival786

With a well-rounded repertoire spanning 16+ years, a hearty portion of my skill set aligns seamlessly with the requirements of your project. As a seasoned database administrator and Python-based ML enthusiast, I am well-versed in handling even the messiest data pipelines. With thorough knowledge of SQL, NumPy, and scikit-learn, I have successfully developed, deployed, and monitored a range of machine learning models to cater to business-oriented needs-an experience that prepares me well for your opening. Moreover, my proficiency in both spoken and written English and Spanish goes well beyond fluent comprehension; it's ingrained within my professional journey. This instils in me a unique understanding of the nuances involved in translating business objectives into practical solutions for our clients- an invaluable asset to any project. Additionally, positive client feedback often commends my precision and proficiency with Airflow, Docker-the very tools essential to your task. Finally, my passion for perfection coupled with an exceptional ability to collaborate makes me an ideal fit for situations where autonomy is equally as important as teamwork. These have been critical traits throughout my time working with more than 750 satisfied clients globally! My portfolio speaks volumes about not just what I can do but also how I get things done. Thank you for considering my application!

@PrateekP3003

I’d approach this as a data + ML engineering foundation, not just individual model development. For an early-stage startup, the priority should be building reliable pipelines and reproducible ML workflows that can move from raw, inconsistent sources to monitored production models. I can contribute across Python, pandas, NumPy, scikit-learn, PyTorch/TensorFlow, advanced SQL, data pipelines, model deployment and monitoring, with cloud-based architecture and workflow orchestration using tools such as Airflow/Prefect. For example, I would structure the initial work around: - Source ingestion, validation, cleaning and transformation - Reproducible feature engineering and dataset preparation - Model training, evaluation, and versioning - Production deployment with monitoring for performance/data drift - Clear documentation so the system remains maintainable as the startup scales Before implementation, I’d want to clarify: What are your current data sources and volumes? Is there already a target ML use case/model, or should we define the first use case from the business objective? What cloud environment and pipeline infrastructure currently exist? I can work independently while keeping the implementation practical and aligned with business outcomes, with clear technical decisions and measurable results. Happy to discuss the current data/ML architecture and define the right first milestone.

@codeandcommand

We handle inventory sync for three warehouses on a similar pipeline, so the ML/data engineering split here is familiar territory. I would set up the Spark jobs for data mining, wrap the model in Docker, and handle the Spanish side of documentation and client calls directly. Can start today, working pipeline within 5 days. Budget and timeline above are starting points based on the post. We will lock in real numbers once we walk through the full scope together. Want me to send a quick plan?

@KasjanovaEste

I looked at your brief and saw you need a mid‑level ML/Data Engineer who can build ingestion pipelines, clean multi‑source data, deploy and monitor models, and document systems while collaborating in English and Spanish. I can design reliable ETL flows, implement Airflow or Prefect DAGs, build and tune models in scikit‑learn, PyTorch or TensorFlow, write advanced SQL for transformations, containerise workloads with Docker, and support early‑stage product needs with clear bilingual communication. I can start right away. Fernando PS. Have question on the project and I would like you to check the clarification board