ML Model Deployment Specialist

Job Overview

We are seeking an experienced Machine Learning Operations Specialist to join our team. As an ML Ops Engineer, you will play a crucial role in bridging the gap between machine learning development and production deployment.

This position requires strong technical skills in cloud infrastructure, continuous integration and continuous deployment (CI/CD), and DevOps, as well as a deep understanding of the full machine learning lifecycle.

Main Responsibilities:

1. Automate model deployment: Design, develop, and implement CI/CD pipelines for automating the deployment of machine learning models to production environments.
2. Model monitoring & management: Build tools to monitor model performance in real-time and trigger automatic re-training when model drift occurs. Ensure models are scalable and optimized for high-performance.
3. Collaboration: Work closely with data scientists and ML engineers to ensure smooth integration of ML models into production, focusing on reliability and scalability.
4. Automation: Automate model training, deployment, and lifecycle management processes to improve efficiency and reduce manual overhead.
5. Cloud & infrastructure management: Manage cloud resources (AWS, GCP, Azure) and infrastructure to ensure high availability, cost optimization, and efficient scaling of ML workloads.
6. Versioning & experiment tracking: Implement versioning systems for model artifacts, datasets, and training environments. Track experiments and ensure reproducibility.
7. Security & compliance: Ensure that the deployed models meet security and regulatory requirements (data privacy, model governance, etc.).
8. Troubleshooting & optimization: Debug and troubleshoot production ML models, and optimize performance issues related to compute, data pipelines, and model inference.

Required Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Experience:
  • 3+ years of experience in deploying and maintaining machine learning models in production environments.
  • Experience working with cloud platforms like AWS, GCP, or Azure, and understanding of cloud-native services such as EC2, Lambda, Kubernetes, etc.

Key Skills:

• Proficiency in programming languages such as Python, R, or Java.
• Strong understanding of machine learning workflows and tools such as TensorFlow, PyTorch, Scikit-learn, or similar.
• Experience with containerization and orchestration technologies (Docker, Kubernetes).
• Familiarity with version control systems (e.g., Git) and ML versioning tools (e.g., DVC, MLflow).
• Expertise in creating and managing CI/CD pipelines using tools like Jenkins, GitLab CI, or CircleCI.
• Experience with automated model testing, validation, and deployment.
• Strong knowledge of infrastructure-as-code (IaC) tools such as Terraform or CloudFormation.
• Monitoring & observability: Experience with monitoring tools (Prometheus, Grafana, ELK stack) and logging frameworks for ML models in production.
• Data engineering: Familiarity with data pipelines (ETL) and distributed computing (Spark, Kafka).
• Collaboration: Strong ability to work with cross-functional teams including data scientists, software engineers, and business stakeholders.

Preferred Qualifications:

• Advanced ML tools: Knowledge of tools like Kubeflow, Seldon, or TFX for automating ML pipelines.
• Data security: Understanding of secure deployment practices for machine learning models, including data privacy and compliance standards (GDPR, HIPAA, etc.).
• Model interpretability: Familiarity with techniques for explaining and interpreting machine learning models (e.g., SHAP, LIME).
• Ai/ml experimentation: Experience with experiment tracking platforms (e.g., Weights & Biases).
• Leadership: Experience mentoring or leading teams in MLOps practices.