Sr ML Platforms Engineer

Senior Machine Learning Platforms Engineer - Bengaluru, India

PayPal is seeking a highly skilled Senior Machine Learning Platforms Engineer to join our dynamic team in Bengaluru, Karnataka, India. This role is pivotal in developing and optimizing machine learning models for a wide array of applications. You will be instrumental in preprocessing and analyzing large datasets to derive actionable insights, and in deploying robust ML solutions into production environments using cutting-edge tools and frameworks.

Key responsibilities include collaborating with cross-functional teams to seamlessly integrate ML models into our products and services, as well as continuously monitoring and evaluating the performance of deployed models. The ideal candidate will possess a minimum of 5 years of relevant work experience, coupled with a Bachelor's degree or equivalent practical experience.

We require extensive experience with major ML frameworks such as TensorFlow, PyTorch, or scikit-learn. Familiarity with cloud platforms like AWS, Azure, and GCP, along with tools for data processing and model deployment, is essential. A proven track record in designing, implementing, and deploying machine learning models is a must.

Exceptional programming skills in Java, including core Java, deep understanding of internal mechanisms, data structures, memory management, and JVM utilization, are critical. You should demonstrate a proven ability to design, deploy, and operationalize AI/ML solutions in both on-premises and cloud environments (GCP, AWS, Azure).

Hands-on experience building scalable data solutions using big data frameworks and distributed storage systems (e.g., Hadoop, Spark, Dataproc) is expected. Proficiency in cloud platforms, especially GCP, and container orchestration tools such as Terraform, Kubernetes, and Helm, is highly valued. Strong expertise in developing predictive machine learning models and implementing large language models (LLMs) is a key requirement.

We are looking for strong proficiency in data modeling, feature engineering, and classical machine learning algorithms including neural networks, linear regression, logistic regression, and random forest. Knowledge of AI/ML security, compliance, and ethical AI practices is also important. Experience with agentic frameworks like Langchain, CrewAI, Langgraph, ADK, and MCP server development will be advantageous. Practical expertise in fine-tuning, serving, and inferencing large language models (LLMs) is required.