Senior Artificial Intelligence/Machine Learning Engineer
Ciklum
Job Overview
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Job Description
About Ciklum
Ciklum is a custom product engineering company that supports both multinational organizations and scaling startups to solve complex business challenges with a global team of over 4,000 skilled professionals.
About the Role
As a Senior Artificial Intelligence/Machine Learning Engineer, you will join a cross-functional development team to build and scale advanced machine learning infrastructure, including real-time and batch inference systems, model deployment pipelines, and predictive platforms to ensure high performance, scalability, and reliability.
Responsibilities
- Mentor and guide mid-level Machine Learning Engineers.
- Conduct comprehensive code reviews and foster continuous learning.
- Translate business requirements into actionable ML solutions.
- Lead integration of new technologies into ML infrastructure.
- Architect and oversee complex machine learning pipelines.
- Collaborate with Data Science teams to optimize models in production.
Requirements
- Expert-level proficiency with AWS and cloud technologies.
- Strong knowledge of containerization (Kubernetes) and MLOps.
- Proven production experience in scalable ML models and services.
- Experience with MLflow, Airflow or Dagster, and stream processing (Kafka).
- Proficient in Python and SQL; additional languages a plus.
Benefits
- Work with top professionals in a friendly, supportive environment.
- Engage in large-scale, globally impactful projects.
- Access tailored learning resources including courses and certifications.
- Enjoy radical flexibility with remote or office work options.
- Receive medical subscription, meal tickets, and other perks.
Key skills/competency
AWS, Kubernetes, Python, SQL, MLflow, Airflow, Data Engineering, Kafka, MLOps, mentoring
How to Get Hired at Ciklum
- Research Ciklum's culture: Study their mission and news.
- Customize your resume: Highlight ML and AWS skills.
- Leverage networking: Connect on LinkedIn with team members.
- Prepare technical answers: Review ML infrastructure case studies.
- Practice interview insights: Discuss past leadership in ML projects.
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