Senior Machine Learning Operations Mlops Engineer

Job Summary

Client is seeking an experienced Machine Learning Engineer to design, build, deploy, and maintain scalable machine learning platforms and production ML systems.

The ideal candidate will have strong hands-on experience in Machine Learning Operations (MLOps), Python, cloud-based ML platforms, CI/CD automation, model deployment, monitoring, and governance.

This role focuses on managing the complete machine learning lifecycle, from data preparation and model training to deployment, monitoring, and automated retraining. The engineer will work closely with data scientists, data engineers, software engineers, and business teams to deliver reliable, secure, and scalable machine learning solutions.

Experience in banking, financial services, or other regulated industries is highly desirable.

Key Responsibilities

1. Machine Learning Platform Engineering

• Design, develop, deploy, and maintain production-grade machine learning solutions.
• Build reusable platforms supporting model development, training, validation, deployment, and monitoring.
• Implement model versioning, experiment tracking, model registries, and reproducibility practices.
• Support both batch processing and real-time inference workloads.
• Optimize ML systems for performance, scalability, reliability, and cost efficiency.

2. MLOps and CI/CD Automation

• Build and maintain automated CI/CD/CT pipelines for machine learning workflows.
• Automate model testing, validation, deployment, rollback, and retraining.
• Implement Infrastructure as Code (IaC) and automated environment provisioning.
• Develop tools for managing the ML lifecycle across development, testing, and production environments.

3. Feature Stores and Data Engineering

• Design and support enterprise feature stores for feature reuse, consistency, and governance.
• Integrate ML solutions with data warehouses, data lakes, and operational data sources.
• Develop scalable data pipelines for model training and inference.
• Improve data quality, availability, lineage, and observability.

4. Model Monitoring and Production Reliability

• Implement monitoring for model performance, data quality, data drift, concept drift, and feature drift.
• Establish monitoring dashboards, alerts, and incident management processes.
• Develop automated model retraining strategies.
• Ensure production ML systems remain reliable, accurate, and efficient.

5. Real-Time Machine Learning

• Build low-latency model scoring and prediction platforms.
• Develop event-driven ML architectures using streaming technologies.
• Support real-time inference pipelines and near real-time business decision-making.

6. Governance, Security, and Compliance

• Implement MLOps governance standards and controls.
• Ensure compliance with enterprise security, privacy, risk, and regulatory requirements.
• Support model auditability, explainability, lineage tracking, and documentation.
• Collaborate with risk, compliance, and cybersecurity teams.

7. Collaboration and Technical Leadership

• Work closely with data scientists, data engineers, analytics teams, and software engineers.
• Translate business requirements into scalable technical solutions.
• Contribute to MLOps standards, reusable frameworks, and engineering best practices.
• Mentor junior engineers and support enterprise adoption of modern ML engineering practices.

Required Qualifications

• 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering.
• At least 3 years of experience building and supporting production machine learning systems.
• Hands-on experience managing the end-to-end machine learning lifecycle.
• Experience deploying and operating ML workloads in AWS, Azure, or Google Cloud.
• Experience supporting both batch and real-time ML solutions.
• Experience working with large-scale structured and unstructured datasets.
• Strong programming skills in Python and SQL.
• Experience with CI/CD automation, model deployment, monitoring, and production support.

Required Technical Skills

Machine Learning and MLOps

• ML lifecycle management
• Model deployment and orchestration
• Experiment tracking and model versioning
• Model Registry and Feature Store
• Model monitoring and observability
• Automated retraining workflows
• Model governance and responsible AI

Cloud Platforms

Experience with one or more of the following:

• AWS SageMaker
• Azure Machine Learning
• Google Vertex AI

Programming and Data Engineering

• Python and SQL
• Pandas and PySpark
• Apache Spark
• dbt
• Snowflake, Databricks, IBM DB2, or equivalent enterprise data platforms
• Data lakes and cloud storage platforms

DevOps and Infrastructure

• Git, GitHub, or GitLab
• Jenkins, GitHub Actions, or Azure DevOps
• Terraform or other Infrastructure as Code tools
• Linux administration and shell scripting
• Docker and Kubernetes
• OpenShift is a plus

Streaming and Real-Time Processing

• Apache Kafka
• Event-driven architectures
• Real-time inference and scoring pipelines

Monitoring and Observability

• Prometheus
• Grafana
• ELK Stack
• Cloud-native monitoring tools

Preferred Qualifications

• Experience implementing enterprise Feature Stores and Model Registries.
• Experience building automated ML governance frameworks.
• Experience developing real-time decisioning and scoring platforms.
• Experience with fraud detection, credit risk, customer analytics, recommendation systems, or predictive analytics.
• Familiarity with MLflow, Kubeflow, Airflow, Argo Workflows, Feast, or SageMaker Pipelines.
• Knowledge of distributed systems and software architecture patterns.
• Familiarity with AI/ML regulatory requirements and model risk management.
• AWS, Azure, or GCP certifications.

Expected Deliverables

The successful candidate will help establish and improve enterprise MLOps capabilities, including:

• Enterprise Feature Store implementation and adoption.
• Model Registry and governance processes.
• Automated CI/CD/CT pipelines.
• Real-time model deployment and scoring capabilities.
• Machine learning monitoring and observability frameworks.
• Reusable ML platform services that accelerate model development and deployment.

Recruiter Screening Guidelines

Must-have skills:

1. Python and SQL
2. MLOps and end-to-end ML lifecycle management
3. Production ML deployment and monitoring
4. AWS SageMaker, Azure ML, or Google Vertex AI
5. CI/CD pipeline automation
6. Model versioning and experiment tracking
7. Data engineering and ML pipeline development

Good-to-have skills:

1. Feature Store and Model Registry implementation
2. MLflow, Kubeflow, or SageMaker Pipelines
3. Docker and Kubernetes
4. Apache Kafka and real-time inference
5. Terraform and Infrastructure as Code
6. Banking or financial services experience