Machine Learning Operations (MLOps) Engineer (Only W2)

🎓Required Qualifications

• 🔹Experience
• 🔹5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
• 🔹3+ years of experience building and supporting production machine learning systems.
• 🔹Demonstrated experience implementing end-to-end machine learning lifecycle management.
• 🔹Experience operating ML workloads in cloud environments (AWS, Azure, or GCP).
• 🔹Experience supporting both batch and real-time machine learning solutions.
• 🔹Experience working with large-scale structured and unstructured data.
• 🔹Required Technical Skills
• 🔹Machine Learning & MLOps
• 🔹ML lifecycle management
• 🔹Model deployment and orchestration
• 🔹Experiment tracking and model versioning
• 🔹Model monitoring and observability
• 🔹Feature Store architecture
• 🔹Model Registry implementation
• 🔹Automated retraining workflows
• 🔹Responsible AI and model governance practices
• 🔹Cloud Platforms

📈Experience with one or more

• 🔹AWS SageMaker
• 🔹Azure Machine Learning
• 🔹Google Vertex AI
• 🔹Programming

📌Proficiency in

• 🔹Python (required)
• 🔹SQL (required)
• 🔹Java, Scala, or R (preferred)

Data Platforms

📈Experience with

• 🔹Snowflake
• 🔹Databricks
• 🔹IBM DB2 or equivalent enterprise RDBMS platforms
• 🔹Data lakes and modern cloud storage platforms
• 🔹Data Transformation

📈Experience with

• 🔹dbt
• 🔹Spark
• 🔹Pandas
• 🔹PySpark
• 🔹DevOps & Infrastructure

📈Experience with

• 🔹Git/GitHub/GitLab
• 🔹Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools
• 🔹Terraform or infrastructure-as-code frameworks
• 🔹Linux administration
• 🔹Shell scripting
• 🔹Containers & Orchestration

📈Experience with

• 🔹Docker
• 🔹Kubernetes
• 🔹OpenShift (preferred)
• 🔹Streaming & Real-Time Technologies

📈Experience with

• 🔹Apache Kafka
• 🔹Event-driven architectures
• 🔹Real-time inference pipelines
• 🔹Monitoring & Observability

📈Experience with

• 🔹Prometheus
• 🔹Grafana
• 🔹ELK Stack
• 🔹Cloud-native monitoring solutions