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PeopleLogic
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ML Ops Engineer

ML Ops Engineer

PeopleLogic

5 years

0 - 6 Lacs

bengaluru

Posted:16 hours ago| Platform: GlassDoor logo

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Skills Required

ml drive learning monitoring ai pipeline management model orchestration collaboration deployment auto scaling rest automate testing sagemaker workflow automation drift analysis data strategies training devops engineering aws postgresql dynamodb sqs api pytorch tensorflow airflow transportation healthcare

Work Mode

On-site

Job Type

Part Time

Job Description

Role : MLOps
Experience : 5 years to 8 years Role Brief: 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 and cloud

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

revolutionizing how the world plans, builds, and manages infrastructure projects with Masterworks, our industry- is setting new standards for project delivery and asset management. Recognized as one of the Top 25 AI Companies of 2024 and a Great Place to Work for three consecutive years, we are leveraging artificial intelligence to create a smarter, more connected future for customers in transportation, water and utilities, healthcare, higher education, and the we don’t just develop software—we shape the future. If you’re excited to join a fast-growing company and collaborate with some of the brightest minds in the industry to solve real-world challenges, let’s connect.

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ML Ops Engineer