MLOps Engineer

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