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Senior Machine Learning Engineer

Senior Machine Learning Engineer

Tredence

5 - 8 years

15 - 30 Lacs

kolkata pune bengaluru

Posted:1 day ago| Platform:

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Skills Required

model deployment google cloud platforms machine learning vertex ai ml pipelines

Work Mode

Work from Office

Job Type

Full Time

Job Description

As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying scalable ML solutions using GCP and Vertex AI. You will work closely with data scientists, engineers, and product teams to operationalize models and manage the full ML lifecycle.

Key Responsibilities:

Model Development & Deployment:

Build, train, and optimize ML models using Vertex AI (AutoML, custom training jobs, Workbench).
Deploy models to production using Vertex AI endpoints and integrate with APIs or downstream systems.

ML Pipelines & MLOps:

Design and implement scalable ML pipelines using Vertex AI Pipelines, Kubeflow, and Cloud Functions.
Apply CI/CD practices using Cloud Build, GitHub Actions, and MLflow.
Monitor model performance, implement drift detection, and automate retraining workflows.

Cloud Infrastructure:

Utilize GCP services such as BigQuery, Dataflow, Cloud Storage, Pub/Sub, and Cloud Functions.
Ensure secure, cost-efficient, and scalable cloud architecture.

Collaboration & Documentation:

Work with cross-functional teams to translate business requirements into ML solutions.
Document workflows, architecture decisions, and model lifecycle processes.

Required Skills:

Technical Expertise:

Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
Experience with orchestration tools like Airflow, Kubeflow, or Argo.
Familiarity with containerization (Docker) and orchestration (Kubernetes).

Cloud & Vertex AI:

Hands-on experience with GCP services and Vertex AI components (AutoML, Pipelines, Model Registry).
Understanding of IAM, VPC, and cloud security best practices.

MLOps & System Design:

Experience in building reproducible, scalable ML systems.
Knowledge of model governance, versioning, and explainability.

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