MLOps Engineer | Kenility

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.