We are seeking a Senior Machine Learning Operations (MLOps) Engineer to design, build, and optimize scalable machine learning platforms and production ML systems.
Candidate must have strong experience building and operationalizing machine learning solutions in cloud environments, developing CI/CD pipelines for ML workloads, and collaborating with data scientists, data engineers, software engineers, and business stakeholders.
Key Responsibilities
ML Platform Engineering & Model Operations
- Design, develop, deploy, and manage production-grade machine learning solutions.
- Build scalable and reusable machine learning platforms that support model development, training, validation, deployment, and monitoring.
- Develop frameworks to support both batch and real-time inference workloads.
- Optimize machine learning systems for performance, reliability, maintainability, and cost efficiency.
MLOps Pipeline Development
- Design and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows.
- Automate model testing, validation, deployment, rollback, and retraining processes.
Feature Store & Data Engineering Integration
- Design and support enterprise feature stores to improve feature reuse, consistency, and model governance.
- Collaborate with data engineering teams to integrate machine learning solutions with data warehouses, data lakes, and operational data sources.
Production Monitoring & Reliability
- Establish alerting, observability, and incident management processes.
- Develop strategies for model retraining and continuous performance improvement.
Real-Time Machine Learning Systems
- Build and support low-latency scoring and prediction platforms.
- Develop event-driven machine learning architectures using streaming technologies.
- Partner with business and engineering teams to enable near real-time decisioning capabilities.
Governance, Security & Compliance
- Implement MLOps governance standards and controls.
- Support model auditability, explainability, lineage tracking, and documentation.
- Partner with risk, compliance, and cybersecurity teams to maintain operational standards.
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
Cloud Platforms
Experience with one or more: AWS SageMaker or Azure Machine Learning or Google Vertex AI
Programming
Proficiency in: Python (required), SQL (required) , Java, Scala, or R (preferred)
Experience with:
- Snowflake
- Databricks
- IBM DB2 or equivalent enterprise RDBMS platforms
- Data lakes and modern cloud storage platforms
Data Transformation
Experience with:
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
Flexible work from home options available.
— Sarah J.
·
Marketing Manager
★★★★★
Verified
