MLOps Engineer (P806)
About Us:
As a Senior MLOps Engineerat Kenility, you’ll join a tight-knit family of creative developers, engineers, and designers, who strive to develop and deliver the highest quality products into the market.
Technical Requirements:
- Bachelor’s degree in Computer Science, Software Engineering, or a related field.
- Proven expertise in DevOps and MLOps environments, including the use of Terraform and CI/CD pipelines.
- Extensive knowledge of AWS infrastructure, with a focus on network, security, and container orchestration.
- Familiarity with Atlassian tools such as Jira, Confluence, and experience managing Bitbucket pipelines.
- Proficient in programming languages including Python, Go, Java, or Scala.
- Strong analytical mindset with meticulous attention to detail.
- Excellent collaboration skills, capable of working across multidisciplinary teams.
- Background working with big data platforms, particularly Hadoop and Spark.
- Hands-on experience with machine learning frameworks and libraries like TensorFlow, XGBoost, and scikit-learn, as well as tools supporting MLOps workflows.
- Demonstrated ability to perform effectively both independently and as part of a team.
- Capacity to manage shifting priorities and juggle multiple responsibilities.
- Deep commitment to high-quality deliverables and accountability in both team and individual settings.
- Holding certifications such as AWS Certified Cloud Practitioner, AWS Certified Machine Learning Engineer, or AWS Certified DevOps Engineer is a strong plus.
- Minimum Upper Intermediate English (B2) or Proficient (C1).
Tasks and Responsibilities:
- Work closely with data scientists, ML engineers, and data engineers to architect scalable and reliable ML pipelines and real-time inference systems.
- Automate the complete machine learning lifecycle, from data ingestion through training, evaluation, deployment, and monitoring.
- Design and manage CI/CD pipelines tailored for ML model integration and deployment.
- Monitor model performance post-deployment, ensuring alignment with business goals and performance criteria.
- Support the configuration and optimization of monitoring systems for deployed models.
- Identify and resolve issues affecting ML model deployment and performance.
- Continuously explore new tools and advancements relevant to MLOps and ML engineering.
Soft Skills:
- Responsibility
- Proactivity
- Flexibility
- Great communication skills