Key Responsibilities:Develop and manage deployment processes for ML models in production.Implement CI/CD pipelines for ML workflows.Monitor model performance, reliability, and accuracy.Optimize infrastructure for ML model training and deployment.Ensure security and compliance of ML workflows.Required Experience & Skills:5+ years in software engineering/DevOps, with 2-3 years in MLOps.Proficiency in Python, CI/CD tools, Docker, Kubernetes, and cloud platforms (AWS/Azure/GCP).Hands-on experience with TensorFlow, PyTorch, or scikit-learn.Strong knowledge of monitoring tools (Prometheus, Grafana, ELK stack).

Role Overview:As a Senior Data Engineer, you will design, develop, and manage scalable data solutions using Databricks, Snowflake, and Azure Synapse. You will optimize data pipelines, ensure automation, and collaborate with cross-functional teams to enhance data accessibility and analytics.Key Responsibilities:Lead end-to-end data engineering projects, ensuring best practices in scalability and performance.Develop and optimize ETL/ELT pipelines using Azure Data Factory, AWS Glue, and Apache Airflow.Design data architectures, schemas, and models for analytics, with DBT as a plus.Work with Big Data tools (Hive, Spark) and cloud-based platforms.Implement CI/CD pipelines and version control for data workflows (nice to have).Ingest data from APIs, ERP systems, and RDBMS for seamless integration.Ensure data quality, governance, and monitoring for accuracy and compliance.Collaborate with teams and mentor junior engineers.Requirements:Expertise in Python & SQL with Data Warehousing & ELT experience.Hands-on experience with Snowflake or Databricks (certification preferred).Strong cloud-based ETL/ELT and orchestration tool knowledge.Experience with APIs, data ingestion, and integration.Strong problem-solving, troubleshooting, and communication skills.Preferred Qualifications:Certification in Snowflake, Databricks, or Azure Data Engineering.Experience in Real-time Data Processing (Kafka is a plus).