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Technologies-expected : Python AWS Tensor Flow Py Torch CI/CD technologies-optional : Spark Airflow Grafana ETL about-project : Technologies Utilized: Programming Languages: Python, Go Machine Learning Frameworks: Tensor Flow, Py Torch Cloud Platforms: AWS Big Data Tools: Spark, Snowflake CI/CD and Orchestration Tools: Github Actions, Airflow Monitoring Tools: Grafana responsibilities : Develop, test, and deploy scalable, low-latency machine learning solutions and pipelines, considering various factors such as data characteristics, problem complexity, and computational resource availability. Research and explore the latest advancements in machine learning platform technologies, pushing the limits of what is achievable with ML, while staying current with industry trends and developments. Experiment with and prototype new ML platforms tailored to specific environments, creating rapid prototypes and proof-of-concepts. Automate ML pipelines using CI/CD principles, promoting consistency, reproducibility, and agility across the development lifecycle. Ensure model performance on unseen datasets, guaranteeing that it generalizes effectively without overfitting. Conduct thorough testing to identify and resolve potential issues, including bias or fairness concerns. Optimize model deployment processes, including unit, integration, and stress testing, ensuring high engineering quality. Design and build the next-generation machine learning infrastructure to support the simultaneous operation of thousands of model training pipelines and billions of daily batch predictions. Work closely with internal ML teams (such as Data Scientists and MLOps teams) to enhance codebase quality and overall product health. requirements-expected : Education: Degree in Computer Science or related field. Experience: Minimum of 2 years of proven industry experience. Programming Skills: Proficient in Python, Go, or other object-oriented programming languages. Strong understanding of data structures, algorithms, and software engineering principles. Knowledge of mainstream ML libraries (e.g., Tensor Flow, Py Torch, Spark ML and/or cloud solutions (e.g., AWS, Sagemaker). Familiarity with CI/CD (e.g., Github Actions, Airflow) and big data tools (e.g., Map Reduce, Spark, Flink, Kafka, Docker, Kubernetes). Database Skills: Experience in SQL and database management, including SQL query optimization. Testing Expertise: Experience with unit testing frameworks. benefits : sharing the costs of sports activities private medical care sharing the costs of professional training & courses remote work opportunities flexible working time integration events coffee / tea drinks employee referral program