MLOps Engineer - Python/Spark

Description :

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 :

- 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 :

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 :

- Masters degree in Computer Science, Machine Learning, Data Engineering, or related field.

Perks, Benefits and Work Culture :

- Competitive Salary Package

- Generous Leave Policy

- Flexible Working Hours

- Performance-Based Bonuses

- Health Care Benefits