About This Role
Summary: 1. Key technical requirements and skills: - Strong experience in Machine Learning and Data Science - Solid understanding of Statistical Modeling and Advanced Statistics - Hands-on experience with Docker and containerized environments - Experience with Kubeflow or other ML orchestration tools - Proficiency in at least one programming language, preferably Python - Strong knowledge of Object-Oriented Programming (OOP) - Experience with CI/CD pipelines and DevOps practices 2. Team/project information: - The position is at Aqilea, a consulting company with a diverse and inclusive workplace. - The role sits at the intersection of data science, software engineering, and DevOps, with a focus on productionizing models and maintaining robust ML pipelines. 3. Unique or notable aspects: - The company offers great opportunities for career growth and the chance to
We are a consulting company with a bunch of tech-savvy and happy people!
We love technology, we love design, and we love quality. Our diversity makes us unique and creates an inclusive and welcoming workplace where every individual is highly valued.
With us, everyone can be themselves while respecting others for who they are. We believe that when an amazing mix of people come together and share their knowledge, experiences, and ideas, we can help our clients on a completely different level.
We are looking for someone who can start immediately and wants to grow with us!
With us, you have great opportunities to make real progress in your career and the chance to take on significant responsibility.
About the Role
We are seeking a highly skilled Machine Learning Engineer / MLOps Engineer to design, build, and deploy scalable machine learning systems. This role sits at the intersection of data science, software engineering, and DevOps, with a strong emphasis on productionizing models and maintaining robust ML pipelines.
Key Responsibilities
- Design, develop, and deploy machine learning models at scale
Build and maintain end-to-end ML pipelines using modern MLOps practices
Containerize applications and workflows using Docker
Orchestrate ML workflows with Kubeflow or similar platforms
Collaborate with data scientists to operationalize statistical and machine learning models
Implement CI/CD pipelines for ML systems and data workflows
Ensure reliability, scalability, and performance of ML infrastructure
Apply advanced statistical modeling techniques to solve complex business problems
Write clean, modular, and maintainable code using object-oriented programming principles
Monitor, evaluate, and continuously improve deployed models
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field
Strong experience in Machine Learning and Data Science
Solid understanding of Statistical Modeling and Advanced Statistics
Hands-on experience with Docker and containerized environments
Experience with Kubeflow or other ML orchestration tools (e.g., Airflow, MLflow)
Proficiency in at least one programming language (Python preferred)
Strong knowledge of Object-Oriented Programming (OOP)
Experience with CI/CD pipelines and DevOps practices
Familiarity with cloud platforms (GCP, or Azure)
Preferred Qualifications
Experience with large-scale distributed systems
Knowledge of feature stores, model versioning, and monitoring tools
Experience in deploying real-time or batch ML systems
Familiarity with infrastructure-as-code (e.g., Terraform)
Understanding of data engineering concepts and big data tools
Key Skills
Machine Learning & Deep Learning
MLOps & Model Lifecycle Management
Docker & Containerization
Kubeflow & Workflow Orchestration
Statistical Analysis & Advanced Modeling
Object-Oriented Programming
CI/CD & DevOps Practices
Originally posted on Himalayas