Job Title: Machine Learning Operations Lead Engineer

Job Summary: We are seeking an experienced MLOps Lead Engineer to design, implement, and manage scalable machine learning infrastructure and deployment pipelines. You will collaborate with data scientists, ML engineers, and DevOps teams to ensure models are production-ready, resilient, and continuously improving. The ideal candidate has a strong background in both software engineering and machine learning operations, with a passion for automation, reliability, and performance.

Key Responsibilities:

Lead the design and implementation of MLOps frameworks and CI/CD pipelines for ML models. Manage the end-to-end ML lifecycle: data ingestion, training, validation, deployment, monitoring, and retraining. Collaborate with data science and engineering teams to operationalize ML models in production. Ensure scalability, security, and compliance in ML workflows. Build and maintain automated systems for model versioning, model registry, and experiment tracking. Monitor ML model performance in production and establish ing and rollback strategies. Mentor and guide junior MLOps and ML engineers.

Required Skills and Qualifications:

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 8+ years of experience in software engineering, DevOps, or MLOps roles (minimum 3+ years of experience in MLOps). Proficiency in ML frameworks (TensorFlow, PyTorch) and MLOps tools (MLflow, Kubeflow, Airflow). Experience with Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure). Strong coding skills in Python and scripting languages like Bash or YAML. Knowledge of model monitoring, data drift detection, and A/B testing. Familiarity with data versioning tools (e.g.,Github, DVC) and model registry tools. Experience in monitoring productized ML models using tools such as Prometheus, Grafana, ELK Stack (Elasticsearch, Logstash, Kibana), AWS SageMaker Model Monitor. Experience with Jenkins, Terraform, and GitHub Actions.