We are seeking a skilled and experienced MLOps Engineer to join our team and drive the operationalization of machine learning models and pipelines at scale. The ideal candidate will be responsible for automating, deploying, monitoring, and maintaining AI/ML solutions. Turning prototypes into robust, customer-ready solutions while mitigating risks like production pipeline failures, will be primary. This role requires expertise in infrastructure management, CI/CD pipelines, cloud services, model orchestration and collaboration with cross-functional teams to ensure seamless deployment into diverse customer environments.

Primary Responsibilities :

– Strategizing and implementing scalable infrastructure for ML or LLM model pipelines using tools like Kubernetes, Docker, 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.

–  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

– Use Infrastructure-as-Code (IaC) tools like Terraform to standardize provisioning deployments

– 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. 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

– Expertise in containerization (Dockers, Kubernetes) for consistent deployments orchestration tools like Airflow, ArgoCD, Kubeflow etc.

– Experience with CI/CD tools (e.g., Jenkins, GitLab CI/CD) and IaC tools like Terraform

– 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)

– AWS Certified Machine Learning – Specialty – Good to have this certification

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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