Machine Learning Engineer

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Shrinq Consulting Group INC, is seeking the following. Apply via Dice today!

The Machine Learning Engineer is responsible for designing, building, deploying, and maintaining scalable machine learning models and systems that power data-driven products and services. This role bridges data science and software engineering, focusing on production-grade ML solutions.

• Design, develop, and train machine learning models for real-world applications
• Deploy models into production environments with scalability and reliability in mind
• Optimize model performance, accuracy, and efficiency

• Build and maintain data pipelines for training, validation, and inference
• Ensure data quality, consistency, and availability
• Collaborate with data engineers and analysts on data infrastructure

• Implement MLOps practices including CI/CD for ML, model versioning, and monitoring
• Track model performance, drift, and retraining needs
• Automate model deployment, testing, and rollback processes

• Work closely with product managers, data scientists, and software engineers
• Translate business requirements into ML-driven solutions
• Support experimentation, A/B testing, and feature iteration

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field

• 3–6 years of experience in machine learning, data science, or software engineering
• Experience deploying ML models in production environments

• Strong programming skills in Python (and/or Java, Scala)
• Experience with ML frameworks (Tensor Flow, PyTorch, Scikit-learn)
• Solid understanding of ML algorithms, statistics, and data structures
• Experience with data processing tools (Pandas, Num Py, Spark)
• Familiarity with APIs and microservices for model serving

• Experience with cloud platforms (AWS, Azure, Google Cloud Platform)
• Familiarity with containerization and orchestration (Docker, Kubernetes)
• Knowledge of CI/CD pipelines and Infrastructure as Code is a plus

• Experience with large-scale or real-time ML systems
• Knowledge of MLOps tools (MLflow, Kubeflow, Sage Maker, Vertex AI)
• Experience with NLP, computer vision, or recommendation systems
• Familiarity with SQL and No
  
  SQL databases
• AI/ML certifications are a plus