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Weekday AI (YC W21)
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MLOps Engineer

MLOps Engineer

Weekday AI (YC W21)

8 years

60 - 70 Lacs

noida uttar pradesh india

Posted:2 days ago| Platform:

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Skills Required

design learning ml model training deployment monitoring management data engineering pipeline inference automation etl apache airflow aws processing spark docker kubernetes testing development governance reliability drift versioning collaboration enablement support devops orchestration redshift workflow logging python tensorflow pytorch git github jenkins linux scripting troubleshooting

Work Mode

On-site

Job Type

Full Time

Job Description

This role is for one of the Weekday's clients

Salary range: Rs 6000000 - Rs 7000000 (ie INR 60-70 LPA)

Min Experience: 8 yearsLocation: Noida, Uttar Pradesh, IndiaJobType: full-timeWe are seeking an experienced

MLOps Engineer

to design, build, and operate scalable, production-grade machine learning platforms and pipelines. This role focuses on operationalizing ML workflows across cloud-native infrastructure, enabling reliable model training, deployment, monitoring, and lifecycle management at scale. You will collaborate closely with data science, data engineering, and platform teams to bring ML solutions into production efficiently and securely.

Requirements

Key Responsibilities

ML Platform & Pipeline Engineering

Design, implement, and manage cloud-native ML platforms supporting model training, inference, and lifecycle automation.
Build and orchestrate ML and ETL pipelines using Apache Airflow / AWS MWAA and distributed processing frameworks such as Apache Spark (EMR/Glue).
Productionize data science workflows, experiments, and notebooks into robust, scalable systems.

Deployment & Automation

Containerize and deploy ML workloads using Docker, Kubernetes (EKS), ECS/Fargate, and AWS Lambda.
Develop and maintain CI/CT/CD pipelines for automated model training, validation, testing, and deployment.
Enable reproducible ML environments using containerized development workflows and Jupyter-based platforms.

Observability, Governance & Reliability

Implement ML observability including data drift, model drift, performance monitoring, and alerting using CloudWatch, Prometheus, and Grafana.
Ensure data governance, metadata management, versioning, lineage, and reproducibility across ML pipelines.
Build secure, reliable, and scalable data pipelines aligned with best practices.

Collaboration & Enablement

Work closely with data scientists to operationalize ML models and experiments.
Partner with data engineering and platform teams to scale ML infrastructure and workflows.
Support best practices around ML lifecycle management and operational excellence.

Required Skills & Experience

8+ years of experience in MLOps, DevOps, or ML platform engineering.
Strong hands-on expertise with AWS, including:

Compute & Orchestration: EKS, ECS, EC2, Lambda
Data Services: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
Workflow Orchestration: Airflow / MWAA, Step Functions
Monitoring & Logging: CloudWatch, OpenSearch, Grafana

Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Strong experience with Docker, Kubernetes, Git, and CI/CD tools (GitHub Actions, Jenkins, etc.).
Solid Linux, scripting, automation, and troubleshooting skills.
Experience enabling reproducible ML environments and containerized development workflows.

Education

Master's degree in Computer Science, Machine Learning, Data Engineering, or a related field.

Skills

MLOps, AWS, Airflow/MWAA, Apache Spark, Kubernetes, Docker, CI/CT/CD, CloudWatch, Glue, ECS

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MLOps Engineer