Lead Machine Learning Operations Engineer
Lead Machine Learning Operations Engineer
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Job Description
We are looking for an experienced and motivated engineer to join our Data Science team within Medication Delivery Solutions (MDS) Business as the lead ML Ops Engineer. As an MLOps Engineer you will play a crucial role in the deployment monitoring and maintenance of machine learning models. You will work closely with data scientists software engineers and IT operations to ensure that our machine learning models are reliable scalable and performing optimally in production environments. Your expertise will be essential in automating and streamlining our ML workflows enhancing model reproducibility and ensuring continuous integration and delivery. The MLOps Engineer will directly report to the Director of Data Science.
Responsibilities:
- Design build and maintain the infrastructure required for efficient development deployment and monitoring of machine learning models.
- Implement CI/CD pipelines for machine learning applications.
Develop and manage cloudbased and onpremises solutions for model training deployment and monitoring. - Ensure the scalability reliability and performance of machine learning systems. Collaborate with data scientists to understand and implement requirements for model serving versioning and reproducibility.
- Monitor and optimize model performance in production identifying and resolving issues proactively.
- Automate repetitive tasks to improve efficiency and reduce the risk of human error. Maintain documentation and provide training to team members on MLOps best practices. Stay updated with the latest developments in MLOps tools technologies and methodologies.
- Communicate and share knowledge with other team members and actively participate in various learningsharing opportunities
Qualifications:
- Bachelors or Masters degree in Computer Science Engineering or a related field.
- 3 years of experience in MLOps DevOps or related fields.
- Strong programming skills in Python with experience in other languages such as Java C or Scala being a plus.
- Experience with ML frameworks such as TensorFlow PyTorch and/or scikitlearn.
- Proficiency with CI/CD tools such as Jenkins or GitLab CI.
- Handson experience with cloud platforms such as AWS Google Cloud or Azure.
- Familiarity with containerization and orchestration tools like Docker and Kubernetes.
- Knowledge of infrastructureascode tools such as Terraform or CloudFormation.
- Strong understanding of machine learning lifecycle including data preprocessing model training evaluation and deployment.
- Excellent problemsolving skills and the ability to work independently as well as part of a team.
- Strong communication skills and the ability to explain complex technical concepts to non technical stakeholders.
Preferred Qualifications:
Experience with feature stores model registries and monitoring tools such as MLflow Tecton or Seldon.
Familiarity with data engineering tools like Apache Spark Kafka or Airflow.
Knowledge of security best practices for machine learning systems.
Experience with A/B testing and model performance monitoring
Location: Flexible with the ability to work overlapping hours with Eastern Standard Time (EST).
Start Date: As soon as possible. However the process will take some time as candidate CV reviews and interviews will be conducted by both Softeq and our client.
Duration: a longterm engagement (12 months or more).
Employment Type
Full Time
