SEARGIN IS HIRING!!
As a dynamic multinational tech company operating in 50 countries, we drive innovation and create projects that shape the future and greatly enhance the quality of life. You will find our solutions in the space industry, supporting scientists in the development of cancer drugs, and implementing innovative technological solutions for industrial clients worldwide. These are just some of the areas in which we operate!
Role Overview
The ML Engineer will be responsible for designing, building, and maintaining machine learning systems, focusing on developing, training, and deploying models. This role includes automating model deployment and management processes to ensure seamless integration with business solutions. The ML Engineer will collaborate with data scientists and other technical teams to develop and integrate analytical models for business use.
Project: Transactional Fraud Detection
You will provide engineering support for supervised machine learning models focused on Real-Time Fraud Detection. The goal is to enhance fraud detection in real-time transactional data, with an emphasis on improving the model’s performance and accelerating the overall development process. You will work closely with data scientists and analytics translators to optimize code for data processing, improve workflow efficiency, and introduce automation, enabling data scientists to focus more on model development.
Key Responsibilities:
- Collaborate with data scientists to optimize codebases for data processing and improve performance.
- Refactor existing workflows to enhance efficiency and introduce automation.
- Implement best practices in software engineering to streamline machine learning pipelines and improve code quality.
- Support the deployment, training, and integration of machine learning models in real-time fraud detection.
- Develop and maintain scalable data processing pipelines using technologies such as Spark, Airflow, and mlFlow.
- Provide ongoing support to ensure smooth ML operations (MLOps), monitoring model performance, and optimizing deployment processes.
Must-Have Skills:
- Proven experience working on machine learning projects.
- Experience with ML frameworks such as Apache Airflow, sklearn, MLFlow, and TensorFlow.
- Strong Python programming skills (Scikit-learn, Pandas, NumPy).
- Solid understanding of MLOps architecture and practices.
- Proficient in data manipulation and transformation (e.g., SQL).
- Experience working in cloud environments (e.g., GCP) and with cloud-based data platforms.
- Familiarity with software engineering best practices such as version control, testing, documentation, and code reviews.
- Experience with automation tools for deployment and provisioning (e.g., Docker, Kubernetes, OpenShift, CI/CD pipelines).
Nice-to-Have Skills:
- Experience working with distributed systems and clusters for batch and streaming data (e.g., S3, Spark, Kafka, Flink).
- Experience with monitoring and observability tools (e.g., ELK stack).
- Affinity with advanced analytics, data science, and NLP.
- Hands-on experience building complex data pipelines (ETL).
- Familiarity with system design and architecture.
- Experience with programming in statically typed languages (e.g., Scala, Java).
- Strong understanding of databases, including relational, non-SQL, and time-series databases.
- Experience working in an agile/scrum development environment.
- Open-source project contribution experience is a plus.
Additional Information:
- Position: Fully remote, B2B contract
- Industry: Fraud detection and financial technology
If you are a passionate ML Engineer who enjoys optimizing workflows, collaborating with data scientists, and working in a dynamic, fully remote environment, we encourage you to apply.
