We are seeking a highly skilled AI Engineer with deep expertise in Machine Learning Operations (MLOps) to join our innovative technology team.
The ideal candidate will combine advanced technical skills in AI/ML with robust infrastructure and deployment capabilities.
Key Responsibilities
Design, develop, and maintain scalable machine learning pipelines, particularly for image processing applications
Implement and optimize end-to-end ML workflows using cutting-edge image editing algorithms
Create robust infrastructure for model inference, validation, deployment, and monitoring using AWS services
Develop and maintain containerized ML applications using Docker
Implement streaming data processing architectures with Apache Kafka
Build automated CI/CD pipelines for machine learning models
Ensure model performance, reliability, and scalability across different environments
Collaborate with data science and software engineering teams to integrate ML models into production systems
Qualifications
> 5 years of experience
Expert-level Python programming
Advanced knowledge of MLOps principles and practices
Extensive experience with AWS cloud services (EC2, S3, Lambda)
Proficiency in Docker for containerization
Good understanding of cutting-edge image editing and computer vision algorithms
Experience with model monitoring, versioning, and lifecycle management
Familiarity with machine learning frameworks (PyTorch etc.)
Knowledge of infrastructure as code (Terraform, CloudFormation)
Background in Apache Kafka for real-time data streaming is a plus
Push the Limits: Strive for ambitious goals with no excuses.
Get it Done: Resilient, lean, and hands-on, with a focus on continuous improvement.
Dream Team: Foster honest feedback, emotional connections, and a “Team 1” mindset prioritizing collective excellence.
Hands-on Leadership: Actively engages with the team, setting an example in delivery and technical execution.
Entrepreneurial Mindset: Operates as a “late cofounding CTO,” taking full ownership of the company’s technical and strategic evolution.
Problem-Solving:
Prioritizes pragmatic, actionable solutions with a scientific approach.
Embraces continuous improvement with a focus on quality and performance.
Stays composed and maintains perspective under pressure, balancing firmness with empathy.
Communication:
Effectively inspires teams, persuades stakeholders, and drives alignment.
Practices radical candor—clear, honest, and pragmatic communication.
Customer-Centricity: Deeply understands client needs and product value delivery.
2 calls (with HR & team members)
1 Business case
1 Call with our CEO
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Reference check
Proposal
These companies are also recruiting for the position of “Data / Business Intelligence”.
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