Machine Leaning Engineering (P2)

About the Role

Responsibilities

• ● Independently build, train, and deploy machine learning models for complex projects. ● Design and maintain robust, end-to-end ML pipelines, from data processing to model serving. ● Contribute to technical design discussions and provide input on system architecture and best practices. ● Collaborate with business, product, and other engineering teams to understand requirements and translate them into technical specifications. ● Write high-quality, production-ready code and participate in code reviews to maintain our standards of excellence. ● Mentor interns or junior engineers, sharing your knowledge and expertise.

Basic Qualifications

• ● Bachelor's or Master's degree in Engineering, Mathematics, Statistics, or a related field. ● 3-6 years of professional experience in machine learning engineering. ● Proven experience in building and deploying ML models in a production environment. ● Strong proficiency in Python, SQL, and experience with a distributed computing framework (e.g., Spark). ● Data Analytics Experience: Strong background in data analytics, including statistical analysis, data visualization, and reporting to derive business insights. ● AI Experience: Practical, hands-on experience with modern AI frameworks and techniques, including LLMs or Generative AI applications. ● Familiarity with workflow orchestration tools (e.g., Airflow) and ML platforms (e.g., MLflow) is preferred. ● Knowledge of classification models, regression models, anomaly detection, boosted models, deep learning, and simulation problems, particularly with large datasets. ● Strong problem-solving skills and the ability to work independently on projects.

Preferred Qualifications / Bonus Skills

• Experience with Generative AI models. Track record of publications in top -tier AI/ML/CV conferences or journals. Experience working with sports data (broadcast feeds, social media imagery, sponsorship analytics). Proficiency in cloud computing platforms (AWS, GCP, Azure) and their AI/ML services. Experience with video processing and analysis techniques. Familiarity with data pipeline and distributed computing tools (e.g., Apache Spark, Kafka). Demonstrated ability to lead technical projects and mentor team members.
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