Jobily
About the job
We are looking for a Machine Learning Ops Engineer for our client. In this role, you will be responsible for designing, implementing, and scaling end-to-end machine learning and AI infrastructure on Databricks. You will contribute to building a unified ML platform that supports multiple business domains, ensuring robust, automated, and scalable ML workflows across the entire lifecycle — from data ingestion and feature engineering to deployment and real-time model serving. You will collaborate closely with data science, platform, and software engineering teams to ensure secure, compliant, and efficient ML operations.
What You’ll Be Doing
Architect, deploy, and maintain ML pipelines for training, evaluation, deployment, and continuous monitoring using Databricks, MLflow, and related tools.
Build and manage CI/CD workflows for ML model versioning, testing, and controlled releases.
Design and operationalize feature pipelines using Databricks Feature Store or similar technologies.
Implement observability and drift detection for both model and data performance using tools such as Evidently AI, Prometheus, and Grafana.
Collaborate with platform teams to optimize GPU/CPU clusters, Delta Live Tables, and Unity Catalog for scalability, reproducibility, and compliance.
Automate infrastructure provisioning using Terraform and Databricks APIs.
Champion best practices in MLOps, AIOps, and ML governance, ensuring secure and efficient model lifecycle management.
Partner with data scientists to transition research models into production with minimal friction.
Evaluate and implement real-time model serving architectures (batch, streaming, or online inference).
What You’ll Bring
4+ years of experience in MLOps, ML Platform Engineering, or similar production-focused ML roles.
Strong background in containerization and orchestration (Docker, Kubernetes).
Proven experience building ML pipelines on Databricks, including MLflow, Delta Lake, Unity Catalog, and Feature Store.
Proficiency in Python, shell scripting, and Git-based development workflows.
Experience with monitoring and observability tools such as Prometheus and Grafana.
Familiarity with streaming data technologies (Kafka, Spark Structured Streaming).
Experience with IaC (Terraform) and workflow orchestration tools (Airflow, Prefect).
Excellent problem-solving, communication, and cross-functional collaboration skills.
Nice to Haves
Experience designing multi-model architectures and scalable model serving systems.
Exposure to LLMOps and generative AI deployment patterns on Databricks or similar platforms.
Knowledge of AIOps tools for intelligent alerting, anomaly detection, and system optimization.
Relevant certifications such as Databricks Certified Machine Learning Professional, Azure AI Engineer Associate, or Azure DevOps Engineer Expert.
Why Join Them?
💸 Competitive salary package.
💻 Career coaching and mentorship to support your professional growth.
🌈 Diverse and multicultural teams that promote inclusivity.
🦄 Outstanding working environment.
🚀 Continuous training and development opportunities.
About the company
Where Talent Meets Opportunity.
At SILVARE, our mission is clear: To empower our clients to thrive in the digital age. Leveraging our extensive experience in leading transformational change and managing day-to-day operations, we’re dedicated to helping organizations create lasting value and elevate their performance across the enterprise.
Your Success, Our Priority. Tailored Talent Solutions.
What we do:
1. Customized Recruitment Solutions
2. Outsourcing Solutions
3. Talent Acquisition Services
4. Recruitment Process Outsourcing
Similar Jobs
About the job
We are looking for a Machine Learning Ops Engineer for our client. In this role, you will be responsible for designing, implementing, and scaling end-to-end machine learning and AI infrastructure on Databricks. You will contribute to building a unified ML platform that supports multiple business domains, ensuring robust, automated, and scalable ML workflows across the entire lifecycle — from data ingestion and feature engineering to deployment and real-time model serving. You will collaborate closely with data science, platform, and software engineering teams to ensure secure, compliant, and efficient ML operations.
What You’ll Be Doing
Architect, deploy, and maintain ML pipelines for training, evaluation, deployment, and continuous monitoring using Databricks, MLflow, and related tools.
Build and manage CI/CD workflows for ML model versioning, testing, and controlled releases.
Design and operationalize feature pipelines using Databricks Feature Store or similar technologies.
Implement observability and drift detection for both model and data performance using tools such as Evidently AI, Prometheus, and Grafana.
Collaborate with platform teams to optimize GPU/CPU clusters, Delta Live Tables, and Unity Catalog for scalability, reproducibility, and compliance.
Automate infrastructure provisioning using Terraform and Databricks APIs.
Champion best practices in MLOps, AIOps, and ML governance, ensuring secure and efficient model lifecycle management.
Partner with data scientists to transition research models into production with minimal friction.
Evaluate and implement real-time model serving architectures (batch, streaming, or online inference).
What You’ll Bring
4+ years of experience in MLOps, ML Platform Engineering, or similar production-focused ML roles.
Strong background in containerization and orchestration (Docker, Kubernetes).
Proven experience building ML pipelines on Databricks, including MLflow, Delta Lake, Unity Catalog, and Feature Store.
Proficiency in Python, shell scripting, and Git-based development workflows.
Experience with monitoring and observability tools such as Prometheus and Grafana.
Familiarity with streaming data technologies (Kafka, Spark Structured Streaming).
Experience with IaC (Terraform) and workflow orchestration tools (Airflow, Prefect).
Excellent problem-solving, communication, and cross-functional collaboration skills.
Nice to Haves
Experience designing multi-model architectures and scalable model serving systems.
Exposure to LLMOps and generative AI deployment patterns on Databricks or similar platforms.
Knowledge of AIOps tools for intelligent alerting, anomaly detection, and system optimization.
Relevant certifications such as Databricks Certified Machine Learning Professional, Azure AI Engineer Associate, or Azure DevOps Engineer Expert.
Why Join Them?
💸 Competitive salary package.
💻 Career coaching and mentorship to support your professional growth.
🌈 Diverse and multicultural teams that promote inclusivity.
🦄 Outstanding working environment.
🚀 Continuous training and development opportunities.
Hybrid
Πληροφορική
Permanent
Full Time
About the company
Where Talent Meets Opportunity.
At SILVARE, our mission is clear: To empower our clients to thrive in the digital age. Leveraging our extensive experience in leading transformational change and managing day-to-day operations, we’re dedicated to helping organizations create lasting value and elevate their performance across the enterprise.
Your Success, Our Priority. Tailored Talent Solutions.
What we do:
1. Customized Recruitment Solutions
2. Outsourcing Solutions
3. Talent Acquisition Services
4. Recruitment Process Outsourcing