We are looking for an experienced Senior MLOps Engineer to join our team and play a key role in building, deploying, scaling, and maintaining machine learning solutions in production environments.
The ideal candidate will have strong expertise in machine learning infrastructure, cloud platforms, automation, CI/CD pipelines, and model lifecycle management.
Key Responsibilities
· Design, build, and maintain scalable MLOps pipelines for end-to-end machine learning workflows.
· Develop and manage CI/CD/CT pipelines for machine learning models.
· Automate model training, validation, deployment, monitoring, and retraining processes.
· Build and maintain ML infrastructure using cloud platforms such as AWS, Azure, or GCP.
· Implement model versioning, experiment tracking, and model registry solutions.
· Monitor model performance, data quality, model drift, and system reliability.
· Collaborate closely with Data Scientists, ML Engineers, Data Engineers, and DevOps teams.
· Optimize ML workloads for performance, scalability, cost, and reliability.
· Implement containerization and orchestration solutions using Docker and Kubernetes.
· Establish best practices for ML model governance, security, and reproducibility.
Requirements & Qualifications
· 5+ years of experience in MLOps, DevOps, Machine Learning Engineering, or a related field.
· Strong programming skills in Python.
· Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
· Strong experience with Docker and Kubernetes.
· Experience building CI/CD pipelines using tools such as GitLab CI, Jenkins, or GitHub Actions.
· Practical experience with ML workflow orchestration tools such as MLflow, Kubeflow, Airflow, or Prefect.
· Experience with cloud platforms (AWS, Azure, or GCP).
· Strong understanding of model deployment, serving, monitoring, and lifecycle management.
· Familiarity with Infrastructure as Code tools such as Terraform.
· Solid understanding of Linux, networking, APIs, and distributed systems.
· Strong problem-solving, communication, and collaboration skills.
Good to Have
· Experience with Large Language Models (LLMs) and Generative AI deployment.
· Familiarity with LLMOps, RAG pipelines, and vector databases.
· Experience with model optimization, GPU infrastructure, and distributed training.
· Knowledge of feature stores and real-time inference systems.
