ML Engineer

San R&D Business Solutions LLC•Sunrise, FL

2d•Hybrid

About The Position

We are seeking an experienced Machine Learning Engineer to join our team on a contract basis. The ideal candidate should have strong expertise in Python development, hands-on experience building and deploying machine learning models, and proven ability to operationalize ML solutions through APIs on Google Cloud Platform (GCP). This role requires end-to-end ownership of ML systems, from data preparation and model development to deployment, monitoring, and integration into production environments.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience)
3–7 years of experience as a Machine Learning Engineer or Python Backend Engineer
Strong proficiency in Python, with ability to write clean, optimized, production-quality code
Hands-on experience with ML libraries such as NumPy, Pandas, Scikit-learn
Experience building, training, and deploying machine learning models
Strong understanding of the machine learning lifecycle and pipelines
Experience developing RESTful APIs or microservices using FastAPI, Flask, or similar frameworks
Practical experience deploying applications and ML models on Google Cloud Platform (GCP)
Familiarity with version control systems (Git)
Strong problem-solving, debugging, and communication skills

Nice To Haves

Experience with PyTorch or TensorFlow
Exposure to Generative AI, Large Language Models (LLMs)
Hands-on experience with LangChain, LangGraph, or similar AI frameworks
Familiarity with AI agent architectures and modern ML frameworks
Experience with cloud-native architectures, containerization, and CI/CD pipelines
Knowledge of vector databases and ML model monitoring tools

Responsibilities

Design, develop, and maintain machine learning models using Python
Perform data preprocessing, feature engineering, model training, evaluation, and optimization
Build and manage end-to-end ML pipelines including training, validation, deployment, and monitoring
Develop, deploy, and maintain RESTful APIs and Python-based microservices to serve ML models
Integrate ML solutions into enterprise applications and workflows
Deploy and manage ML workloads on Google Cloud Platform (GCP)
Utilize GCP services such as Vertex AI, Cloud Run, Cloud Functions, BigQuery, and Cloud Storage
Ensure scalability, performance, reliability, and security of deployed solutions
Collaborate with cross-functional teams including product, data, and engineering
Troubleshoot and resolve issues across the ML and application stack