MLOps Engineer

Role Overview :

As an MLOps Engineer, you will be responsible for bridging the gap between machine learning development and production deployment. You will work closely with data scientists, ML engineers, and IT operations to ensure that ML models are deployed, monitored, and scaled effectively.

This role involves both technical skills in cloud infrastructure, CI/CD, and DevOps, as well as an understanding of the full machine learning lifecycle

Key Responsibilities :

- Model Deployment : Design, develop, and implement CI/CD pipelines for automating the deployment of machine learning models to production environments.

- 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.

- Collaboration : Work closely with data scientists and ML engineers to ensure smooth integration of ML models into production, focusing on reliability and scalability.

- Automation : Automate model training, deployment, and lifecycle management processes to improve efficiency and reduce manual overhead.

- Cloud & Infrastructure Management : Manage cloud resources (AWS, GCP, Azure) and infrastructure to ensure high availability, cost optimization, and efficient scaling of ML workloads.

- Versioning & Experiment Tracking : Implement versioning systems for model artifacts, datasets, and training environments. Track experiments and ensure reproducibility.

- Security & Compliance : Ensure that the deployed models meet security and regulatory requirements (data privacy, model governance, etc.).

- Troubleshooting & Optimization : Debug and troubleshoot production ML models, and optimize performance issues related to compute, data pipelines, and model inference.

Required Qualifications :

Education : Bachelors or Masters 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.

Technical 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.