Senior MLOps Engineers

At DVT, a leading global custom software development and data engineering company, we are seeking passionate Java developers to join our innovative and collaborative team! This is an opportunity to work alongside some of the most skilled professionals in the industry, leveraging cutting-edge technologies and best practices to deliver world-class solutions.

At DVT, you’ll be part of a culture that fosters continuous learning and growth. We support your professional development with comprehensive training programs and sponsor various industry events like DevConf and GDG. Join us and push the boundaries of what’s possible, while taking your career to new heights!

Position: MLOps Engineer

We are looking for an experienced MLOps Engineer to join our team. The ideal candidate will have a strong background in machine learning, cloud infrastructure, and DevOps practices. The MLOps Engineer will collaborate with data scientists, machine learning engineers, and software developers to deploy, scale, monitor, and manage machine learning models in production. This role requires a blend of software engineering, data science, and operations knowledge.

Key Responsibilities - MLOps:

1. Lead the MLOps charge
2. Collaborate with data scientists and machine learning engineers to deploy models in production environments using CI / CD pipelines.
3. Build and maintain infrastructure for machine learning model training, validation, deployment, and monitoring.
4. Implement best practices for data versioning, model versioning, and continuous integration / continuous deployment (CI / CD) pipelines.
5. Optimize and automate workflows for machine learning pipelines, ensuring scalability, efficiency, and reproducibility.
6. Monitor machine learning models in production for performance drift, accuracy, and degradation.
7. Work with cloud platforms (e.g., AWS, GCP, Azure) to set up secure and scalable environments for machine learning workloads.
8. Implement monitoring tools and logging solutions for ML model performance, ensuring quick identification and resolution of production issues.
9. Develop automated testing and validation scripts to ensure the integrity and reliability of models in production.
10. Collaborate with cross-functional teams to improve the end-to-end lifecycle of machine learning projects.
11. Ensure compliance with security and data privacy standards throughout the machine learning lifecycle.

Knowledge and Skills:

1. Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Engineering, or a related field.
2. 3+ years of experience in MLOps, DevOps, or Data Engineering.
3. Strong experience with cloud platforms such as AWS, Google Cloud, or Azure.
4. Proficiency in containerization and orchestration tools (Docker, Kubernetes).
5. Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
6. Familiarity with version control tools (Git, DVC) and CI / CD pipelines (Jenkins, GitLab CI).
7. Experience with infrastructure-as-code tools (Terraform, CloudFormation).
8. Proficiency in scripting languages such as Python, Bash, or Scala.
9. Experience in monitoring tools (Prometheus, Grafana, ELK Stack) for model and infrastructure health checks.
10. Understanding of model training, validation, and deployment processes.

Preferred:

1. Experience with automated model retraining and optimization.
2. Familiarity with data engineering workflows including ETL, data pipelines, and data governance.
3. Experience with feature stores and model repositories.
4. Knowledge of A / B testing and model performance tracking (MLflow, TFX, SageMaker, or similar).
5. Certifications in cloud computing (AWS, GCP, or Azure).

Key Skills:

1. Strong problem-solving skills and ability to work independently.
2. Excellent communication skills for cross-functional collaboration.
3. Strong understanding of data governance, security, and privacy issues.
4. Ability to work in an agile, fast-paced environment.