MLOps Engineer

Role

Primary Responsibilities:

• Strategizing and implementing scalable infrastructure for ML or LLM model pipelines using tools like and cloud services such as AWS (e.g., AWS Batch, Fargate, Bedrock)
• Manage auto-scaling mechanisms to handle varying workloads and ensure high availability of Rest APIs
• Automate CI/CD pipelines and Lambda functions for model testing, deployment, and updates, reducing manual errors and improving efficiency.
• Amazon SageMaker Pipelines for end-to-end ML workflow automation. Optimize utilizing step-functions
• Conduct drift analysis to detect and respond to data drift, concept drift, and label drift. Implement mitigation strategies such as automated alerts, model retraining triggers, and performance audits.
• Set up reproducible workflows for data preparation, model training, and deployment.
• Provision and optimize cloud resources (e.g., GPUs, memory) to meet computational demands of large models like those used in RAG systems
• Automate retraining workflows to keep models updated as data evolves
• Work closely with data scientists, ML engineers, and DevOps teams to integrate models into production environments.
• Implement monitoring tools to track model performance and detect issues like drift or degradation in real- time. Monitoring dashboards with real-time alerts for pipeline failures or performance issues C Implementing Model Observability frameworks.

Required Skills:

• Education Any Engineering (BE/Btech/ME/Mtech)

• Min 4 years of experience with AWS services such as Lambda, Bedrock, Batch with Fargate, RDS (PostgreSQL), DynamoDB, SQS, CloudWatch, API Gateway, SageMaker
• Should have hands-on experience in drift analysis, including detecting and mitigating data, concept, and label drift in production ML systems
• Knowledge of ML frameworks (e.g., PyTorch, TensorFlow) to understand model requirements during deployment
• Experience with Rest API Frameworks like Fast APIs, Flask
• Familiarity with model observability like Evidently, Nanny ML, Phoenix and monitoring tools (Grafana etc) and retraining tools like MLflow/ Kubeflow / Airflow
• AWS Certified Machine Learning Specialty

  Good to have this certification