MLOps Engineer

AdTech Industry

MLOps Engineer

Peak Hire Solutions

Company

Home

MLOps Engineer

at AdTech Industry

Agency job

via Peak Hire Solutions

8 - 12 yrs

₹60L - ₹80L / yr

Noida

Skills

Apache Airflow

Apache Spark

AWS CloudFormation

DevOps

MLOps

Machine Learning (ML)

Python

EMR

Amazon Web Services (AWS)

ECS

AWS Lambda

athena

Amazon Redshift

Amazon S3

Docker

grafana

prometheus

CI/CD

GitHub

Jenkins

Jupyter Notebook

Scripting

TensorFlow

PyTorch

Review Criteria:

Strong MLOps profile
8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
Must have hands-on Python for pipeline & automation development
4+ years of experience in AWS cloud, with recent companies
(Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

Hands-on in Docker deployments for ML workflows on EKS / ECS
Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
Experience with CI / CD / CT using GitHub Actions / Jenkins.
Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

CV Attachment is mandatory
Please provide CTC Breakup (Fixed + Variable)?
Are you okay for F2F round?
Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

8+ years in MLOps/DevOps with strong ML pipeline experience.
Strong hands-on experience with AWS:
Compute/Orchestration: EKS, ECS, EC2, Lambda
Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
Workflow: MWAA/Airflow, Step Functions
Monitoring: CloudWatch, OpenSearch, Grafana
Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
Strong Linux, scripting, and troubleshooting skills.
Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

Master’s degree in computer science, Machine Learning, Data Engineering, or related field.

Review Criteria:

Strong MLOps profile
8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
Must have hands-on Python for pipeline & automation development
4+ years of experience in AWS cloud, with recent companies
(Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

Hands-on in Docker deployments for ML workflows on EKS / ECS
Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
Experience with CI / CD / CT using GitHub Actions / Jenkins.
Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

CV Attachment is mandatory
Please provide CTC Breakup (Fixed + Variable)?
Are you okay for F2F round?
Have candidate filled the google form?

Role & Responsibilities:

Key Responsibilities:

Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

8+ years in MLOps/DevOps with strong ML pipeline experience.
Strong hands-on experience with AWS:
Compute/Orchestration: EKS, ECS, EC2, Lambda
Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
Workflow: MWAA/Airflow, Step Functions
Monitoring: CloudWatch, OpenSearch, Grafana
Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
Strong Linux, scripting, and troubleshooting skills.
Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

Master’s degree in computer science, Machine Learning, Data Engineering, or related field.

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