DevOps Engineer

2 weeks ago

India PeopleLogic Full time

Details:

🔹 Role: MLops Engineer/Devops Engineer

🔹 Experience: 5-10 Years

🔹 Notice Period: Immediate

🔹 Mode of Interview: Virtual

🔹 Location: Remote


Key Responsibilities:


1. Machine Learning Operations (MLOps) & Deployment

  • Build, deploy, and manage ML models in production using AWS SageMaker,

AWS Lambda, and other relevant AWS services.

  • Develop automated pipelines for model training, validation, deployment, and

monitoring to ensure high availability and low latency.

  • Implement best practices for CI/CD in ML model deployment and manage

versioning for seamless updates.


2. Infrastructure Development & Optimization

  • Design and maintain scalable, efficient, and secure infrastructure for machine

learning operations using AWS services (e.g., EC2, S3, SageMaker, ECR,

ECS/EKS).

  • Leverage containerization (Docker, Kubernetes) to deploy models as

microservices, optimizing for scalability and resilience.

  • Manage infrastructure as code (IaC) using tools like Terraform, AWS

CloudFormation, or similar, ensuring reliable and reproducible environments.


3. Model Monitoring & Maintenance

  • Set up monitoring, logging, and alerting for deployed models to track model

performance, detect anomalies, and ensure uptime.

  • Implement feedback loops to enable automated model retraining based on

new data, ensuring models remain accurate and relevant over time.

  • Troubleshoot and resolve issues in the ML pipeline and infrastructure to

maintain seamless operations.


4. AWS Connect & Integration

  • Integrate machine learning models with AWS Connect or similar services for

customer interaction workflows, providing real-time insights and automation.

  • Work closely with cross-functional teams to ensure models can be easily

accessed and utilized by various applications and stakeholders.


5. Collaboration & Stakeholder Engagement

  • Collaborate with data scientists, engineers, and DevOps teams to ensure

alignment on project goals, data requirements, and model deployment

standards.

  • Provide technical guidance on MLOps best practices and educate team

members on efficient ML deployment and monitoring processes.

  • Actively participate in project planning, architecture decisions, and

roadmapping sessions to improve our ML infrastructure.


6. Security & Compliance

  • Implement data security and compliance measures, ensuring all deployed

models meet organizational and regulatory standards.

  • Apply appropriate data encryption and manage access controls to safeguard

sensitive information used in ML models.


Qualifications:

Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a

related field.

Experience: 3+ years of experience as an MLOps Engineer, DevOps Engineer, or

similar role focused on machine learning deployment and operations.

Technical Skills:

  • Strong expertise in AWS services, particularly SageMaker, EC2, S3, Lambda,

and ECR/ECS/EKS.

  • Proficiency in Python, including ML-focused libraries like scikit-learn and data

manipulation libraries like pandas.

  • Hands-on experience with containerization tools such as Docker and

Kubernetes.

  • Familiarity with infrastructure as code (IaC) tools such as Terraform or AWS

CloudFormation.

  • Experience with CI/CD pipelines, Git, and version control for ML model

deployment.


MLOps & Model Management: Proven experience in managing large ML projects,

including model deployment, monitoring, and maintenance.

AWS Connect & Integration: Understanding of AWS Connect for customer

interactions and integration with ML models.

Soft Skills: Strong communication and collaboration skills, with the ability to explain

technical concepts to non-technical stakeholders.

Preferred Skills:

  • Experience with data streaming and message queues (e.g., Kafka, AWS Kinesis).
  • Familiarity with monitoring tools like Prometheus, Grafana, or CloudWatch for

tracking model performance.

  • Knowledge of data governance, security, and compliance requirements related to ML

data handling.

  • Certification in AWS or relevant cloud platforms.


Work Schedule:

This role requires significant overlap with CST time zone to ensure real-time collaboration

with the team and stakeholders based in the U.S. Flexibility is key, and applicants should be

available for meetings and work during U.S. business hours.

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