MLOps Engineer

Description :

Job Description : ML Ops Engineer

About the Job :

We are looking for an experienced ML Ops Engineer who can seamlessly bridge machine learning development with scalable production deployment.

The ideal candidate should bring strong expertise in CI/CD, containerization, cloud platforms, and ML model lifecycle management, with hands-on experience deploying models into production using modern MLOps frameworks and cloud-native tools such as AWS EKS, SageMaker, Lambda, and related services.

This role requires a deep understanding of ML engineering workflows, model versioning, data pipelines, and the distribution of responsibilities across ML Ops vs. core Machine Learning functions.

Key Responsibilities :

1. Model Lifecycle Management :

- Onboard new ML models into the existing ML platform with standardized training automation and deployment workflows.

- Design and manage model retraining, model updates, and model versioning strategies.

- Collaborate with Data Scientists to convert research prototypes into production-ready models.

- Ensure models comply with performance, governance, and monitoring standards.

2. CI/CD & Deployment Operations :

- Build, maintain, and optimize CI/CD pipelines for Search and ML services using modern DevOps tooling.

- Manage containerized model deployments using Docker, Kubernetes, AWS EKS, or equivalent platforms.

- Run deployment sign-off processes, ensuring daily release validations, version audits, and production readiness.

- Automate infrastructure provisioning and ML workflow orchestration using tools like Terraform, Airflow, or Kubeflow.

3. Big Data Pipeline Management :

- Maintain and enhance training data pipelines, ensuring efficiency, reliability, and cost optimization across big data ecosystems.

- Work with distributed processing tools such as Spark, Hive, EMR, or Glue for training dataset preparation.

- Monitor and optimize data flows to support continuous model improvement and retraining.

4. Cloud Technologies & ML Tooling :

- Utilize AWS cloud services such as SageMaker (training, inference, pipelines), EKS, Lambda, S3, CloudWatch, ECR, and Step Functions.

- Implement model deployment best practicesA/B testing, blue/green deployments, shadow deployments.

- Apply model monitoring for drift, performance degradation, and anomaly detection.

5. Cross-Functional Collaboration :

- Work closely with ML engineers, data scientists, DevOps engineers, and product teams.

- Provide guidance on the distribution of responsibilities between machine learning development and operationalization layers.

- Offer insights on scaling ML systems and optimizing cloud costs.

Required Skills & Experience :

- 3 to 8 years of experience in ML Ops, DevOps for ML, or Machine Learning engineering.

- Strong hands-on experience with CI/CD, GitOps workflows, and automated deployment pipelines.

- Expertise in containerization (Docker) and orchestration tools like Kubernetes, AWS EKS, or similar.

- Experience deploying ML models into production environments (real-time or batch).

- Proficiency in at least one programming language : Python, Go, or Java.

- Strong understanding of cloud platforms (preferably AWS) and related ML tools such as SageMaker.

- Experience with model tracking, versioning, and lifecycle tools (MLflow, SageMaker Model Registry, DVC).

- Familiarity with big data technologies : Spark, Hadoop, EMR, Glue, Hive, etc.

- Experience creating scalable and resilient ML pipelines.

Preferred Qualifications :

- Experience with feature stores, model observability tools, or real-time ML serving.

- Understanding of Responsible AI, model governance, and compliance frameworks.

- Experience with A/B testing or model experimentation platforms.