Description

You will own the end-to-end development and deployment of production-grade AI systems, including voice, vision, and predictive models.

Responsibilities

• Design and implement scalable data processing pipelines using Spark, Flink, Druid, and Nifi for ingestion, transformation, and optimization.
• Develop and maintain production-quality code for core ML algorithms, including NLP, graph algorithms, statistical ML, and deep learning.
• Build and deploy cloud-native architectures using Kubernetes and Docker, ensuring high availability and performance.
• Implement real-time data streaming solutions with Kafka and other messaging systems to support low-latency model inference.
• Create scalable APIs and data visualization tools to expose model outputs and insights to downstream consumers.

Required Skills

• 12+ years of experience in software engineering with a focus on machine learning and data engineering.
• Hands-on experience with deep learning frameworks Pytorch and Tensorflow.
• Proven track record of setting up at least one end-to-end deep learning pipeline from data ingestion to deployment.
• Strong proficiency in NoSQL databases and experience with large-scale data processing.
• Expertise in Kubernetes and Docker for containerization and orchestration.
• Experience with real-time streaming technologies, specifically Kafka.
• Ability to produce clean, maintainable, and scalable production code.

Key Skills

Education

Any Graduate