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Cognizant
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MLOPS Engineer - Hyderabad (In-Person)

MLOPS Engineer - Hyderabad (In-Person)

Cognizant

6 - 11 years

15 - 30 Lacs

hyderabad

Posted:2 weeks ago| Platform:

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Skills Required

ci cd pipeline big data workflow machine learning airflow github aks code pipeline kubeflow terraform azure cloud gcp mlflow fastapi aws codepipeline aws kubernetes

Work Mode

Hybrid

Job Type

Full Time

Job Description

Title: MLOPS Engineer (Senior / Manager)

Experience:

Location:

Interview Date:

Mode of Interview:

Note:

Job Summary:

We are seeking a skilled and proactive MLOps Engineer to join our AI/ML team. The ideal candidate will be responsible for designing, implementing, and maintaining robust MLOps pipelines and infrastructure across cloud platforms. You will collaborate closely with data scientists, software engineers, and DevOps teams to operationalize machine learning models and ensure scalable, secure, and automated deployment and monitoring.

Key Responsibilities:

Design and implement model deployment, monitoring, and retraining pipelines.
Build and maintain CI/CD/CT pipelines for ML workflows using tools like MLFlow, Kubeflow, Airflow, GitHub Actions, and AWS CodePipeline.
Develop and manage inference, monitoring, and drift detection pipelines (data drift, model drift).
Architect scalable and secure MLOps infrastructure using Kubernetes, AKS, and Terraform.
Publish and manage REST APIs for model inference using FastAPI.
Track experiments and model performance metrics.
Collaborate with cross-functional teams to raise MLOps maturity across the organization.
Conduct internal training and presentations on MLOps tools and best practices.

Required Skills & Technologies:

Cloud Platforms: AWS SageMaker, Azure ML Studio, GCP Vertex AI
Big Data & Processing: PySpark, Azure Databricks
MLOps Tools: MLFlow, Kubeflow, Airflow, GitHub Actions, AWS CodePipeline
Infrastructure & Automation: Kubernetes, AKS, Terraform, FastAPI
Programming Languages: Python (ML and automation), Bash, Unix CLI

Qualifications:

Bachelor's or Masters degree in Computer Science, Data Science, or related field.
Proven experience in operationalizing ML models using MLOps frameworks.
Strong understanding of ML/AI concepts and hands-on experience in model development.
Experience with cloud-native development and container orchestration.
Familiarity with agile methodologies and DevOps practices.

Preferred Qualifications:

Certification in AWS, Azure, or GCP.
Experience with DataRobot, DKube, or similar platforms.
Exposure to enterprise-level ML systems and governance.

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