Machine learning Engineer II - GenAI studios

Key Responsibilities:

• Possess deep knowledge of core systems and takes full responsibility for the code, design, and project delivery, ensuring high quality and on-time completion
• Build and evolve end-to-end ML lifecycle systems from data processing to model serving, using technologies spanning big data frameworks to ML platforms (TensorFlow, PyTorch)
• Design and implement sophisticated monitoring systems that track both technical health and quality metrics
• Create advanced experimentation frameworks that enable rapid A/B testing of algorithms
• Design and develop low-latency, high-throughput serving systems with appropriate caching strategies
• Establish MLOps best practices using MLflow for experiment tracking, model versioning, and deployment automation
• Build stream processing pipelines for real-time feature computation that power dynamic content personalization
• Scale and optimize training and serving of machine learning models, including specialized solutions for embedding scaling
• Collaborate with Product Managers and cross-functional teams to understand requirements and deploy high-quality software solutions that meet business objectives

Skills & Attributes for Success

• Strong coding skills in Python, Java, Golang, or Scala
• Experience with Big Data frameworks like Spark and Kubernetes for data and ML workloads
• Proficient in implementing API performance monitoring solutions using tools such as Grafana, Prometheus, and Cloudwatch
• Skilled in developing end-to-end machine learning pipelines ensuring consistency between development and production environments
• Ability to design scalable ML architectures tailored to site traffic and feature complexity for predictive algorithms
• Familiar with model and data versioning, resource allocation and scaling, and logging to build optimal systems
• Experienced in creating systems that monitor and react to faults in resources, data streams, and model responses
• Expertise with MLOps tools for scalable, production-level deployment, including feature stores, model hosting and versioning, data versioning, prediction and drift monitoring, and automated remediation
• Strong problem-solving skills and the ability to work effectively in a fast-paced environment
• Commitment to continuous learning and professional development

Preferred Education & Experience

• B.Tech/B.E/ Masters or equivalent degree. 
• At least 2-4+ years of professional experience in building and operating machine learning systems at scale