Introduction:

We are seeking a Machine Learning Operations (MLOps) Engineer to join our team. The MLOps Engineer will be responsible for building and maintaining the infrastructure that enables reliable deployment, monitoring, governance, and continuous improvement of production machine learning systems across enterprise client environments.

What You'll Do:

• Design, build, and maintain scalable machine learning deployment pipelines.
• Develop standardized model registries, artifact repositories, data versioning, and reproducible ML environments.
• Build automated evaluation pipelines for production machine learning models.
• Implement automated data quality monitoring including profiling, anomaly detection, validation, quarantine, and alerting.
• Develop automated retraining workflows, promotion gates, rollback capabilities, and audit trails.
• Monitor production environments for model drift, latency, prediction quality, infrastructure performance, and operational costs.
• Troubleshoot production machine learning issues and lead incident response activities.
• Build CI/CD pipelines supporting enterprise AI applications.
• Collaborate closely with Data Scientists and client engineering teams to deploy and maintain AI solutions.
• Ensure governance, security, lineage, reproducibility, and audit readiness across machine learning platforms.

Who You Are:

• Passionate about building reliable AI infrastructure at enterprise scale.
• Experienced deploying and maintaining production machine learning systems.
• Strong analytical and troubleshooting skills.
• Fast learner with attention to detail.
• Excellent communication and collaboration skills.
• Comfortable working with both software engineering and data science teams.

Education: Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related technical field.

Related Work Experience: 3 years supporting production machine learning platforms or cloud infrastructure.

Technical Skills:

• Advanced SQL
• Python
• Apache Spark / PySpark
• AWS SageMaker (Databricks, Azure ML, or Vertex AI experience is a plus)
• Kubernetes
• CI/CD pipelines
• Infrastructure as Code (Terraform, CloudFormation, or similar)
• Model monitoring and ML observability tools
• Data validation and automated testing frameworks
• Statistics related to monitoring, model drift, and performance evaluation
• Git and modern DevOps practices

Our Purpose and Culture

At BDIPlus, we empower organizations to unlock the full potential of their data through AI, advanced analytics, and intelligent enterprise platforms. We partner with Fortune 500 organizations to build scalable data, AI, and machine learning solutions that solve complex business challenges and drive measurable business outcomes.

Innovation is at the core of everything we do. From modern data engineering and AI-powered products to cloud-native architectures and enterprise automation, our teams work on cutting-edge technologies that transform how businesses operate. We foster a collaborative culture where curiosity is encouraged, ideas are valued, and every employee has the opportunity to make a meaningful impact.

Working at BDIPlus offers:

• The opportunity to work on enterprise-scale AI, machine learning, and data platform initiatives.
• A diverse, collaborative, and highly innovative team.
• Exposure to modern cloud technologies and cutting-edge AI platforms.
• Continuous learning and professional development opportunities.
• Full health and commuter benefits.
• Competitive salary and annual bonus.
• Standard paid time off, sick leave, and company holidays.
• Visa sponsorship.
• A culture that encourages creativity, ownership, collaboration, and continuous innovation.