Hire Pre-vetted Machine Learning Engineers

What is a Machine Learning Engineer?

A Machine Learning Engineer is a specialist who designs, develops, and deploys machine learning systems at scale—transforming data science models into production systems that drive real-world business value. ML Engineers do more than run Jupyter notebooks—they build data pipelines, optimize models for production performance, deploy models reliably, monitor model performance over time, and ensure ML systems remain accurate and robust. Whether you need someone to productionize data science models, build ML infrastructure, or scale AI capabilities, a skilled ML Engineer brings deep systems thinking and production expertise.

What makes ML Engineers valuable is their ability to bridge the gap between data science and production reality. They understand that real-world ML is far more than algorithms—it's data pipelines, monitoring, retraining, and reliability. They build systems designed to work reliably in production, not just in development environments. This is why successful AI organizations invest in ML Engineers. When you hire through Torc, you're getting someone who builds ML systems that create lasting business value.

Technology Stack

Machine Learning Frameworks

• TensorFlow & Keras

• PyTorch

• Scikit-learn

• XGBoost & LightGBM

ML Infrastructure & Platforms

• MLflow for ML lifecycle management

• Kubeflow for ML on Kubernetes

• Airflow for workflow orchestration

• Model registries & serving platforms

Data Pipeline & Processing

• Spark & distributed processing

• Kafka & streaming data

• ETL/ELT tools

• Feature stores

Model Deployment & Serving

• TensorFlow Serving

• Seldon Core

• FastAPI for model serving

• Model containerization & Kubernetes

Monitoring & Maintenance

• Model monitoring & drift detection

• A/B testing frameworks

• Retraining pipelines

• Performance metrics & logging

Key Qualities to Look For on a Machine Learning Engineer

Production Mindset — They build systems designed for production reliability. They care about latency, scalability, monitoring, and maintainability over time.

Systems Design — They design end-to-end ML systems including data pipelines, model training, deployment, and monitoring. They understand distributed systems and scaling.

Problem Solving — They troubleshoot ML systems systematically. They diagnose data issues, model degradation, and infrastructure problems.

Data Engineering — They build robust data pipelines. They understand data quality, data versioning, and reliable data processing.

Collaboration — They work closely with data scientists, data engineers, and software engineers. They understand different perspectives and build systems that work for everyone.

Continuous Improvement — They monitor model performance over time, identify when retraining is needed, and continuously improve systems.

Project Types Your ML Engineers Handle

ML Pipeline Development — Building end-to-end ML pipelines. Real scenarios: Data processing pipelines, model training pipelines, automated retraining systems.

Model Deployment & Serving — Deploying models into production and serving them reliably. Real scenarios: Model containerization, serving infrastructure, API deployment.

Infrastructure & Platform Building — Building ML infrastructure and platforms. Real scenarios: MLOps platform development, Kubernetes setup for ML, feature store implementation.

Performance Optimization — Optimizing ML systems for performance and cost. Real scenarios: Model optimization, inference optimization, computational efficiency.

Monitoring & Maintenance — Monitoring ML systems and maintaining model quality. Real scenarios: Model drift detection, retraining automation, performance monitoring.

Data Pipeline Building — Building robust data pipelines for ML. Real scenarios: Feature engineering pipelines, data validation, data quality assurance.

ML System Architecture — Designing ML system architecture and scaling. Real scenarios: End-to-end system design, scaling challenges, reliability patterns.

Interview questions

Question 1: "Walk me through an ML system you built from data pipeline through model serving. What were the key components and what challenges did you face?"

Why this matters: Tests end-to-end ML systems thinking. Reveals whether they understand production ML beyond model training. Shows systems engineering maturity.

Question 2: "Tell me about a time a model's performance degraded in production. How did you detect it, what caused it, and how did you fix it?"

Why this matters: Tests production ML experience and monitoring mindset. Reveals whether they anticipate issues or only react. Shows understanding of model drift and maintenance.

Question 3: "Describe your experience with MLOps practices. What tools and processes have you used to make ML systems reliable and maintainable?"

Why this matters: Tests adoption of MLOps best practices. Reveals whether they treat ML as research or production code. Shows maturity in reproducibility and reliability.