ML Ops Engineer

Role Brief:

We are seeking a skilled ML Ops Engineer to design, implement, and maintain scalable machine learning and large language model (LLM) pipelines in cloud environments, primarily using AWS services. This role is critical to ensuring the reliability, efficiency, and performance of ML systems in production.

The ideal candidate will have hands-on experience with AWS tools such as SageMaker, Lambda, Bedrock, Batch with Fargate, and infrastructure components like RDS, DynamoDB, and SQS. You will be responsible for automating CI/CD workflows, managing auto-scaling APIs, and provisioning cloud resources to support high-performance ML workloads, including RAG systems.

Primary Responsibilities:

• Strategizing and implementing scalable infrastructure for ML or LLM model pipelines using tools like and cloudservices 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 errorsand 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 ModelObservability frameworks.

Required Skills:

• Education Any Engineering (BE/Btech/ME/Mtech)
• Min 5 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