Key Responsibilities:
- Design, build, and deploy end-to-end
machine learning models
using
Azure Machine Learning
services.
- Develop and maintain
MLOps pipelines
for model training, testing, deployment, and monitoring.
- Work with
Azure Databricks, Azure Data Factory, Azure Synapse, and Azure Blob/Data Lake
for data ingestion and processing.
- Implement
CI/CD pipelines
for ML workflows using
Azure DevOps
, GitHub Actions, or similar tools.
- Deploy ML models as
REST APIs/endpoints
using Azure ML managed endpoints, AKS, or containers.
- Monitor model performance, drift, and retraining processes in production.
- Collaborate with data scientists to operationalize ML models and optimize performance.
- Ensure best practices in
security, scalability, governance, and cost optimization
in Azure.
- Build reusable components for feature engineering, model training, and inference.
- Troubleshoot production issues related to ML pipelines and deployed models.

Required Skills:
- 4–7 years of overall IT experience with strong exposure to
Machine Learning and Azure Cloud
.




- Hands-on experience with
Azure Machine Learning Studio / Azure ML Service
.
- Solid knowledge of
Python
, SQL, and ML libraries such as
Scikit-learn, Pandas, NumPy
.
- Experience with
MLOps frameworks
, model versioning, and experiment tracking.
- Knowledge of
Docker, Kubernetes/AKS
, and containerized deployments.
- Experience in
Azure DevOps
, Git, and CI/CD pipeline implementation.
- Familiarity with
data pipelines
and ETL workflows on Azure.
- Understanding of model monitoring, retraining, and lifecycle management.
- Good knowledge of
supervised/unsupervised learning
, feature engineering, and model evaluation techniques.
- Strong problem-solving and communication skills.

Preferred Skills:
- Experience with
Azure Databricks
and
PySpark
.
- Knowledge of
LLMs, Generative AI, or Azure OpenAI
integration is a plus.
- Familiarity with
Terraform/ARM templates/Bicep
for infrastructure automation.
- Experience

📌 Azure ML (Kolkata)
🏢 PwC India
📍 Kolkata