Senior ML Engineer

A leading large-scale mobile platform is seeking a talented and experienced Senior Machine Learning Engineer to become part of her innovative AI team. The perfect candidate will have strong background in machine learning, data engineering, and software engineering. This role will involve taking the lead in designing, developing, and deploying advanced machine learning pipelines and systems.

Responsibilities:

• System Design and Architecture: Build and maintain efficient ML pipelines for data collection, preprocessing, and analysis to support model training and monitoring.
• Research and Innovation: Stay updated on machine learning advancements and apply new techniques to enhance model performance.
• Mentorship and Leadership: Guide and mentor junior engineers and data scientists, leading project teams to success.
• Cross-Functional Collaboration: Work with data scientists, software engineers, architects, and product managers to integrate ML solutions into production systems.

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 4+ years as an ML Engineer or Big Data Developer.
• Programming Skills: Proficient in Python or Java/Scala; experienced with ML frameworks like TensorFlow, PyTorch, and Scikit-Learn.
• Software Development: Knowledge of best practices, including version control, testing, and code reviews.
• Architecture: Understanding of microservices and event-driven design.
• Streaming Technologies: Hands-on experience with Kafka, Kinesis, and SQS.
• MLOps: Familiarity with model registry, experiment tracking (MLFlow, W&B), feature store management (Feast, Tecton), workflow management (Kubeflow, Airflow), and serving platforms (Seldon Core, KServe, SageMaker).
• Big Data: Knowledge of Apache Spark, Trino, Ray, Dask, data lake management (Iceberg, Delta), and data warehouses (Redshift, BigQuery, Snowflake).
• Cloud Computing: Expertise in cloud platforms such as AWS, Google Cloud Platform, or Azure, along with experience in containerization technologies such as Docker and Kubernetes. - advantage
• Strong statistical and analytical skills, with a solid grasp of statistical modeling, machine learning algorithms, data structures, and data analysis techniques.