This is a remote position.
We are seeking a highly experienced Machine Learning Engineer to design, deploy, and maintain production-ready machine learning systems. This role focuses on taking models from experimentation to scalable, monitored, and secure production environments.
You will collaborate with Data Scientists, Data Engineers, and Platform teams to operationalize ML models, build robust data pipelines, and implement MLOps best practices. The ideal candidate understands the full ML lifecycle, including model training, validation, deployment, monitoring, retraining, and governance.
This is not a research-only role. We are looking for engineers who have deployed models into real-world production systems.
Key Responsibilities:
• Design and implement scalable ML systems for real-time and batch inference
• Build model deployment pipelines using containerization and CI/CD
• Develop APIs and services for serving machine learning models
• Implement monitoring and alerting for model performance, drift, and data quality
• Collaborate with Data Engineers to ensure reliable feature pipelines
• Manage model versioning, reproducibility, and governance
• Optimize inference performance and cloud cost efficiency
• Support retraining workflows and continuous improvement
• Ensure security and compliance standards for data and models
Requirements
Requirements
• 4+ years of experience in Machine Learning Engineering or Applied ML
• Strong programming skills in Python
• Hands-on experience with PyTorch, TensorFlow, or similar frameworks
• Experience deploying models into production (API-based, batch, or streaming)
• Experience with Docker and containerized environments
• Familiarity with Kubernetes for scaling ML services
• Experience with MLOps tools (MLflow, model registry, CI/CD integration)
• Strong understanding of feature engineering and data preprocessing
• Experience working in AWS, Azure, or GCP environments
• Knowledge of monitoring, logging, and observability tools
Advanced / Preferred Qualifications
• Experience with distributed training or large-scale data processing
• Experience with feature stores or vector databases
• Experience deploying LLM-powered applications or RAG systems
• Experience implementing model drift detection and automated retraining
• Understanding of security, IAM, and data governance for ML systems
• Experience in high-availability production environments
Ideal Candidate Profile
The ideal candidate:
• Has moved models from notebook to production
• Understands both ML and software engineering principles
• Can explain system trade-offs clearly
• Has worked on systems serving real users or business-critical workflows
• Thinks about reliability, cost, and scalability
