Machine Learning Engineer

StaffXpert LLC; United States; Presencial; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

StaffXpert LLC is seeking a Machine Learning Engineer to design, develop, deploy, and optimize ML solutions powering data-driven products and business decisions. You will build end-to-end ML pipelines, train and fine-tune models, and deploy scalable solutions in cloud environments.

Collaborate with data scientists and software engineers to translate requirements into ML solutions, implement feature engineering, monitor models, and maintain MLOps workflows for versioning and retraining.

Formación

• Proficient in Python and ML frameworks with hands-on experience.
• Experience building end-to-end ML pipelines and deploying models.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with data preprocessing, feature engineering, and model evaluation.
• SQL proficiency and ability to work with large-scale datasets.
• Experience deploying ML solutions in cloud environments (AWS/Azure/GCP).
• Familiarity with Docker, Kubernetes, and CI/CD pipelines.
• Strong software engineering practices and version control (Git).
• Excellent problem-solving and analytical skills.

Responsabilidades

• Design, build, and deploy machine learning models for production applications.
• Develop and maintain scalable data pipelines for model training, validation, and inference.
• Collaborate with data scientists, software engineers, and stakeholders to translate requirements into ML solutions.
• Fine-tune, optimize, and evaluate machine learning and deep learning models.
• Implement feature engineering, model monitoring, and performance tracking processes.
• Build and maintain MLOps workflows for model deployment, versioning, and retraining.
• Work with structured and unstructured data to improve model accuracy and business outcomes.
• Perform experimentation, A/B testing, and model validation.
• Ensure solutions meet scalability, reliability, security, and performance requirements.
• Stay current with advancements in ML, AI, and cloud technologies.

Conocimientos

Python programming

Supervised learning

Unsupervised learning

Data preprocessing

Feature engineering

Model evaluation

Problem solving

Herramientas

PyTorch

TensorFlow

Scikit-learn

SQL

AWS

Azure

GCP

Docker

Kubernetes

CI/CD

Git

Vector databases

Embeddings

Descripción del empleo

We are seeking a Machine Learning Engineer to design, develop, deploy, and optimize machine learning solutions that power data-driven products and business decisions. The ideal candidate will have experience building end-to-end ML pipelines, training and fine-tuning models, and deploying scalable solutions in cloud environments.

Key Responsibilities

• Design, build, and deploy machine learning models for production applications.
• Develop and maintain scalable data pipelines for model training, validation, and inference.
• Collaborate with data scientists, software engineers, and business stakeholders to translate requirements into ML solutions.
• Fine-tune, optimize, and evaluate machine learning and deep learning models.
• Implement feature engineering, model monitoring, and performance tracking processes.
• Build and maintain MLOps workflows for model deployment, versioning, and retraining.
• Work with structured and unstructured data to improve model accuracy and business outcomes.
• Perform experimentation, A/B testing, and model validation.
• Ensure solutions meet scalability, reliability, security, and performance requirements.
• Stay current with advancements in machine learning, AI, and cloud technologies.

Required Skills

• Strong programming experience in Python.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with data preprocessing, feature engineering, and model evaluation.
• Knowledge of SQL and working with large-scale datasets.
• Experience deploying ML solutions in cloud environments such as AWS, Azure, or GCP.
• Familiarity with Docker, Kubernetes, and CI/CD pipelines.
• Understanding of software engineering best practices and version control systems (Git).
• Strong problem-solving and analytical skills.

Preferred Qualifications

• Experience with LLMs, Generative AI, RAG, Agents, or NLP applications.
• Hands-on experience with model fine-tuning techniques such as LoRA, PEFT, or RLHF.
• Experience building and maintaining MLOps platforms.
• Knowledge of distributed training and large-scale ML systems.
• Experience with vector databases, embeddings, and semantic search.