ML Engineer: Scalable Production Models & MLOps

Precise Analytics LLC; Richmond (VA); On-site; USD 120,000 - 190,000; Full time

Job summary

Precise Analytics LLC seeks a skilled machine learning engineer to design and deploy scalable models and pipelines. You will work with large datasets to build classification, regression, NLP, and time series solutions, collaborating with data engineers for data collection and feature engineering.

The role emphasizes production-ready models with MLOps practices and ongoing evaluation, tuning, and retraining, plus applying state-of-the-art techniques.

Qualifications

• Bachelor’s or Master’s in Computer Science, Data Science, ML or related field.

Responsibilities

• Design and implement scalable ML models and pipelines.
• Work with large-scale datasets to develop classification, regression, NLP, and time series forecasting solutions.
• Collaborate with data engineers to ensure proper data collection, transformation, and feature engineering.
• Deploy models to production using MLOps practices (Docker, CI/CD, model versioning).
• Conduct model performance evaluation, tuning, and retraining cycles.
• Research and apply state-of-the-art techniques and open-source frameworks.
• Communicate findings to stakeholders with clear visualizations and reports.
• Support proposal development and client engagements involving AI/ML scopes.

Skills

Python programming

ML problem solving

Communication

Documentation

Statistics knowledge

Data structures

Education

Bachelor’s or Master’s degree in Computer Science/Data Science/Machine Learning or related field

Tools

scikit-learn

TensorFlow

PyTorch

XGBoost

AWS

Azure

GCP

Docker

Kubernetes

MLflow

SageMaker

Vertex AI

Hugging Face Transformers

SpaCy

Spark

Job description

Precise Analytics LLC seeks a skilled machine learning engineer to design and deploy scalable models and pipelines. You will work with large datasets to build classification, regression, NLP, and time series solutions, collaborating with data engineers for data collection and feature engineering.

The role emphasizes production-ready models with MLOps practices and ongoing evaluation, tuning, and retraining, plus applying state-of-the-art techniques.