AI Big Data Enablement Engineer
Apple
AI Big Data Enablement Engineer
Job Description
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. Apple Pay brought mobile payment to millions of customers, and it’s just the beginning. We are looking for engineers who enjoy both hands-on technical work and designing thoughtful, scalable services for Wallet and Apple Pay. Our team’s vision is to be the engine of intelligent transformation, leveraging a unified, reliable data platform to build and deploy innovative and solutions that drive significant business impact and enable data-driven decision-making throughout the organization. We are seeking pragmatic AI Big Data Enablement Engineers to join our dynamic team to build and optimize data and analytics solutions as well as perform ML enablement and participate in generative AI initiatives and and specialize in implementing Developer Enablement technologies leveraging reusable and scalable frameworks to craft the future of Wallet and Apple Pay. You will collaborate with cross-functional teams across different time zones and contribute to scalable data architectures.
Locations
- Hyderabad, Telangana, India
Salary
Skills Required
- hands-on technical workintermediate
- designing thoughtful, scalable servicesintermediate
- leveraging a unified, reliable data platformintermediate
- build and deploy innovative solutionsintermediate
- data-driven decision-makingintermediate
- build and optimize data and analytics solutionsintermediate
- ML enablementintermediate
- generative AI initiativesintermediate
- implementing Developer Enablement technologiesintermediate
- leveraging reusable and scalable frameworksintermediate
- collaborate with cross-functional teamsintermediate
- scalable data architecturesintermediate
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science or a related technical field or equivalent experience (experience)
- 4+ years of experience in designing, developing and deploying data engineering or analytics engineering solutions (experience, 4 years)
- Strong proficiency in SQL, Scala, Python, or Java, with hands-on experience in data pipeline tools (e.g., Apache Spark, Kafka, Airflow), CI/CD practices, and version control. (experience)
- Familiarity with cloud platforms (AWS, Azure, GCP), big data technologies and data analytics tools like Snowflake, Databricks and Tableau. (experience)
- Familiarity with RAG-LLM solutions, GenAI models, APIs, and prompt engineering. (experience)
- Expertise in CI/CD tools like Jenkins, GitHub Actions. (experience)
- Strong analytical skills to optimize developer workflows. (experience)
Preferred Qualifications
- Expertise in building and refining large-scale data pipelines, as well as developing tools and frameworks for data platforms. (experience)
- Hands-on experience with big data technologies such as distributed querying(Trino), real-time analytics(OLAP), near-real-time data processing(NRT), and decentralized data architecture (Apache Mesh). (experience)
- Experience enabling ML pipelines including automating the data flow for feature engineering, model retraining, performance monitoring models in production, drift detection and ensuring scalability. (experience)
- Familiarity with GenAI concepts like Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), prompt engineering, vector embeddings, and LLM fine-tuning. (experience)
- Familiarity with observability tools like DataDog, Prometheus, Grafana. (experience)
- Expertise in building user journey workflow and test suite automation. (experience)
- Familiarity with data governance, security protocols, and compliance. (experience)
- Proven ability to work independently, escalate blockers, and propose thoughtful, long-term solutions. (experience)
- Demonstrates sound judgment, applies technical principles to complex projects, evaluates solutions, and proposes new ideas and process improvements. (experience)
- Seeks new opportunities for growth, demonstrates a thorough understanding of technical concepts, exercises independence in problem-solving, and delivers impactful results at the team level. (experience)
- * Familiarity with Fintech, Wallet domain, digital commerce etc.. (experience)
Responsibilities
- We are seeking pragmatic AI Big Data Enablement Engineers to join our dynamic team to build and optimize data and analytics solutions as well as perform ML enablement and participate in generative AI initiatives and and specialize in implementing Developer Enablement technologies leveraging reusable and scalable frameworks to craft the future of Wallet and Apple Pay. You will collaborate with cross-functional teams across different time zones and contribute to scalable data architectures.
- Design, develop, and maintain scalable data pipelines and architectures for Wallet, Payments & Commerce products.
- Enable data frameworks to automate domain-specific data and analytics workflows.
- Incorporate GenAI into data engineering workflows, applications, or internal data and analytics tools.
- Optimize ETL workflows to enhance data processing efficiency and reliability.
- Ensure data integrity and performance in big data workflows.
- Build frameworks to automate test suites and streamline data operations.
- Develops and deploys AI-powered support tools for developers, such as code suggestion bots, to help identify vulnerabilities and ensure policy compliance.
- Integrate data pipelines into the broader ML Operations (MLOps) process. This includes automating the data flow for feature engineering, model retraining, performance monitoring models in production, drift detection and ensuring scalability.
- Integrates GenAI with CI/CD pipelines to enhance monitoring, alerting, and issue resolution.
- Automates code review processes to detect bugs, enforce coding standards, and suggest improvements.
- Creates technical specifications, instrumentation specs and API contracts.
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Answer 10 quick questions to check your fit for AI Big Data Enablement Engineer @ Apple.
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AI Big Data Enablement Engineer
Apple
AI Big Data Enablement Engineer
Job Description
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. Apple Pay brought mobile payment to millions of customers, and it’s just the beginning. We are looking for engineers who enjoy both hands-on technical work and designing thoughtful, scalable services for Wallet and Apple Pay. Our team’s vision is to be the engine of intelligent transformation, leveraging a unified, reliable data platform to build and deploy innovative and solutions that drive significant business impact and enable data-driven decision-making throughout the organization. We are seeking pragmatic AI Big Data Enablement Engineers to join our dynamic team to build and optimize data and analytics solutions as well as perform ML enablement and participate in generative AI initiatives and and specialize in implementing Developer Enablement technologies leveraging reusable and scalable frameworks to craft the future of Wallet and Apple Pay. You will collaborate with cross-functional teams across different time zones and contribute to scalable data architectures.
Locations
- Hyderabad, Telangana, India
Salary
Skills Required
- hands-on technical workintermediate
- designing thoughtful, scalable servicesintermediate
- leveraging a unified, reliable data platformintermediate
- build and deploy innovative solutionsintermediate
- data-driven decision-makingintermediate
- build and optimize data and analytics solutionsintermediate
- ML enablementintermediate
- generative AI initiativesintermediate
- implementing Developer Enablement technologiesintermediate
- leveraging reusable and scalable frameworksintermediate
- collaborate with cross-functional teamsintermediate
- scalable data architecturesintermediate
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science or a related technical field or equivalent experience (experience)
- 4+ years of experience in designing, developing and deploying data engineering or analytics engineering solutions (experience, 4 years)
- Strong proficiency in SQL, Scala, Python, or Java, with hands-on experience in data pipeline tools (e.g., Apache Spark, Kafka, Airflow), CI/CD practices, and version control. (experience)
- Familiarity with cloud platforms (AWS, Azure, GCP), big data technologies and data analytics tools like Snowflake, Databricks and Tableau. (experience)
- Familiarity with RAG-LLM solutions, GenAI models, APIs, and prompt engineering. (experience)
- Expertise in CI/CD tools like Jenkins, GitHub Actions. (experience)
- Strong analytical skills to optimize developer workflows. (experience)
Preferred Qualifications
- Expertise in building and refining large-scale data pipelines, as well as developing tools and frameworks for data platforms. (experience)
- Hands-on experience with big data technologies such as distributed querying(Trino), real-time analytics(OLAP), near-real-time data processing(NRT), and decentralized data architecture (Apache Mesh). (experience)
- Experience enabling ML pipelines including automating the data flow for feature engineering, model retraining, performance monitoring models in production, drift detection and ensuring scalability. (experience)
- Familiarity with GenAI concepts like Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), prompt engineering, vector embeddings, and LLM fine-tuning. (experience)
- Familiarity with observability tools like DataDog, Prometheus, Grafana. (experience)
- Expertise in building user journey workflow and test suite automation. (experience)
- Familiarity with data governance, security protocols, and compliance. (experience)
- Proven ability to work independently, escalate blockers, and propose thoughtful, long-term solutions. (experience)
- Demonstrates sound judgment, applies technical principles to complex projects, evaluates solutions, and proposes new ideas and process improvements. (experience)
- Seeks new opportunities for growth, demonstrates a thorough understanding of technical concepts, exercises independence in problem-solving, and delivers impactful results at the team level. (experience)
- * Familiarity with Fintech, Wallet domain, digital commerce etc.. (experience)
Responsibilities
- We are seeking pragmatic AI Big Data Enablement Engineers to join our dynamic team to build and optimize data and analytics solutions as well as perform ML enablement and participate in generative AI initiatives and and specialize in implementing Developer Enablement technologies leveraging reusable and scalable frameworks to craft the future of Wallet and Apple Pay. You will collaborate with cross-functional teams across different time zones and contribute to scalable data architectures.
- Design, develop, and maintain scalable data pipelines and architectures for Wallet, Payments & Commerce products.
- Enable data frameworks to automate domain-specific data and analytics workflows.
- Incorporate GenAI into data engineering workflows, applications, or internal data and analytics tools.
- Optimize ETL workflows to enhance data processing efficiency and reliability.
- Ensure data integrity and performance in big data workflows.
- Build frameworks to automate test suites and streamline data operations.
- Develops and deploys AI-powered support tools for developers, such as code suggestion bots, to help identify vulnerabilities and ensure policy compliance.
- Integrate data pipelines into the broader ML Operations (MLOps) process. This includes automating the data flow for feature engineering, model retraining, performance monitoring models in production, drift detection and ensuring scalability.
- Integrates GenAI with CI/CD pipelines to enhance monitoring, alerting, and issue resolution.
- Automates code review processes to detect bugs, enforce coding standards, and suggest improvements.
- Creates technical specifications, instrumentation specs and API contracts.
Tags & Categories
Answer 10 quick questions to check your fit for AI Big Data Enablement Engineer @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs