Staff Machine Learning Engineer

PayPal · Bengaluru · 5+ yrs experience · Posted 2026-07-01

Tech stack: AWS, Azure, GCP

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About the role

Responsibilities:
- Lead the development and optimization of advanced machine learning models.
- Oversee the preprocessing and analysis of large datasets.
- Deploy and maintain ML solutions in production environments.
- Collaborate with cross-functional teams to integrate ML models into products and services.
- Monitor and evaluate the performance of deployed models, making necessary adjustments.
- Partner with Product and Platform Engineering teams to build scalable, secure, and innovative machine learning-powered data products, and large-scale data processing solutions that improve customer experiences and business outcomes.
- Collaborate closely with Data Engineering, Analytics, and Platform teams to ensure high-quality datasets, reliable feature pipelines, and robust monitoring infrastructure.
- Design, build, and manage robust data pipelines and data products that transform complex, multi-source datasets into actionable business insights.
- Oversee data ingestion, preprocessing, transformation, feature engineering, and analysis of large-scale structured and unstructured datasets.
- Leverage large-scale datasets to perform exploratory analysis, feature engineering, anomaly detection, and pattern discovery to identify emerging fraud threats and business opportunities.
- Develop and optimize risk rules, and mitigation strategies using advanced analytics, machine learning, experimentation techniques.
- Drive best practices in modern data architecture, data engineering, streaming and batch processing, and scalable design for high-volume transaction environments.
- Communicate analytical findings, technical recommendations, and business impact effectively to stakeholders, executives, and senior leadership.
- Lead, mentor, and develop high-performing teams of Data Scientists, Machine Learning Engineers, and Data Engineers while fostering innovation and technical growth.
Qualifications:
- 5+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
- Extensive experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
- Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
- 6+ years of experience in Data Engineering, Data Science, Machine Learning Engineering, Fraud Analytics, Risk Modeling, or related disciplines.
- Experience designing and deploying AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, and intelligent automation workflows
- Familiarity with Agentic AI frameworks and orchestration platforms such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, or similar technologies.
- Strong experience designing and supporting large-scale data platforms, distributed systems, and production-grade data pipelines.

Qualifications

- 5+ years relevant experience and a Bachelor’s degree OR Any equivalent combination of education and experience.
- Extensive experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn.
- Expertise in cloud platforms (AWS, Azure, GCP) and tools for data processing and model deployment.
- 6+ years of experience in Data Engineering, Data Science, Machine Learning Engineering, Fraud Analytics, Risk Modeling, or related disciplines.
- Experience designing and deploying AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, and intelligent automation workflows
- Familiarity with Agentic AI frameworks and orchestration platforms such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, or similar technologies.
- Strong experience designing and supporting large-scale data platforms, distributed systems, and production-grade data pipelines.

Responsibilities

- Lead the development and optimization of advanced machine learning models.
- Oversee the preprocessing and analysis of large datasets.
- Deploy and maintain ML solutions in production environments.
- Collaborate with cross-functional teams to integrate ML models into products and services.
- Monitor and evaluate the performance of deployed models, making necessary adjustments.
- Partner with Product and Platform Engineering teams to build scalable, secure, and innovative machine learning-powered data products, and large-scale data processing solutions that improve customer experiences and business outcomes.
- Collaborate closely with Data Engineering, Analytics, and Platform teams to ensure high-quality datasets, reliable feature pipelines, and robust monitoring infrastructure.
- Design, build, and manage robust data pipelines and data products that transform complex, multi-source datasets into actionable business insights.
- Oversee data ingestion, preprocessing, transformation, feature engineering, and analysis of large-scale structured and unstructured datasets.
- Leverage large-scale datasets to perform exploratory analysis, feature engineering, anomaly detection, and pattern discovery to identify emerging fraud threats and business opportunities.
- Develop and optimize risk rules, and mitigation strategies using advanced analytics, machine learning, experimentation techniques.
- Drive best practices in modern data architecture, data engineering, streaming and batch processing, and scalable design for high-volume transaction environments.
- Communicate analytical findings, technical recommendations, and business impact effectively to stakeholders, executives, and senior leadership.
- Lead, mentor, and develop high-performing teams of Data Scientists, Machine Learning Engineers, and Data Engineers while fostering innovation and technical growth.