About the Role
We're looking for a proactive and hands-on MLOps Engineer to join our R&D team and drive internal innovation. In this role, you will work closely with software engineers to research and operationalize cutting-edge models and build robust infrastructure that accelerates experimentation and enables scalable deployment of ML solutions.
Your primary mission will be to build, operationalize, and maintain machine learning models that help our teams extract insights from data faster and develop proof-of-concepts more efficiently across departments. This is a unique opportunity to be at the center of internal experimentation developing reusable ML components, automating analysis workflows, and turning raw data into powerful internal tools that accelerate decision-making.
Responsibilities
- Collaborate with analysts, product managers, and engineers to understand internal data needs and POC requirements.
- Design, build, and maintain scalable MLOps infrastructure (CI/CD, model versioning, orchestration, monitoring).
- Create reusable pipelines and tools for faster model development and experimentation.
- Streamline and automate the process of generating visualizations, dashboards, and reports using ML-based techniques.
- Set up infrastructure and practices for model tracking, versioning, and reproducibility (e. g., MLflow, DVC).
- Ensure reproducibility and traceability of experiments and models across environments.
- Monitor and troubleshoot model performance in staging and production environments.
- Manage and optimize GPU/compute environments, data storage solutions, and ML model serving layers.
- Implement testing, validation, and rollback mechanisms for ML models.
- Stay current with MLOps trends and best practices; introduce tools and practices that improve team productivity.
- Support PoC projects with rapid prototyping and deployment of smart data solutions.
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in DevOps, MLOps, or infrastructure engineering in a production setting.
- Proficiency with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Strong experience with Python and ML frameworks (e. g., TensorFlow, PyTorch, Scikit-learn).
- Familiarity with ML lifecycle tools like MLflow, DVC, Weights, and Biases etc.
- Solid understanding of data pipelines, version control, and CI/CD systems (e. g., GitHub Actions, Jenkins).
Nice-to-Have
- Experience working in an R&D or experimental environment.
- Exposure to distributed training, model compression, or edge deployment.
- Experience building internal tools, dashboards, or data products used by non-technical teams.
- Ability to communicate ML concepts clearly to non-technical stakeholders.
- Comfortable in fast-paced, iterative environments with shifting priorities.