Summary
Would you like to be part of a team that impacts millions of users every single day? Does building highly scalable data-driven systems to tackle real-world problems with a customer-experience centric focus excite you? Do you lose sleep pondering about the algorithm that accurately predicted your recommendation? If so, we'd love to hear from you.
We are looking for a highly skilled AI/ML Engineer with strong hands-on experience in Machine Learning and Generative AI to design, develop, and deploy intelligent solutions for modern contact center platforms that have direct and measurable Product Quality impact to Apple. You will work on building scalable AI systems that power chatbots, voice assistants, speech analytics, and automated customer support workflows. You will collaborate with product managers, data scientists, and platform engineers to implement end-to-end AI pipelines, including model development, deployment, and optimization. The role requires deep expertise in LLMs, conversational AI, and real-time inference systems used in enterprise customer service environments.
Description
Apple has a strong customer first approach and believes in quality delivery for every product. We explore new technology trends and find exciting opportunities to generate innovative solutions. In this role, you will work on the Gen AI Solutions team in Apple’s contact center to enable customers to reach Apple seamlessly through chat and voice channels with Apple advisors. The focus will be to enhance and enrich the messaging ecosystem for the best contact center experience. The role involves working with leaders, business partners and multiple engineering teams on the defined roadmap and delivering on the broken down goals, building and driving innovative solutions to challenging problems end to end and working on exciting new technologies. We support a collaborative work environment, while allowing solution autonomy on projects.
Minimum Qualifications
5+ years of experience applying AI/ML and Gen AI techniques to real business problems
Strong programming experience in Python and proficiency with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn
General software development skills (source code management, debugging, testing, deployment etc.)
Hands-on experience with Generative AI and Large Language Models (LLMs) including GPT models, Llama, Mistral, or similar architectures
Experience building LLM-powered applications including RAG pipelines, agentic workflows, and tool-using models
Knowledge of NLP techniques including transformers, embeddings, semantic search, intent detection, entity recognition, text summarization
Strong understanding of embeddings, vector search, hybrid retrieval, and semantic ranking
Experience integrating LLM models into production systems and enterprise applications
Experience implementing Retrieval Augmented Generation (RAG) pipelines using vector databases such as Milvus or similar, building RAG architectures, hybrid search (vector + keyword), and document chunking strategies
Understanding of text preprocessing, chunking strategies, and embedding optimization for LLM pipelines
Prior experience in implementation of industry solution with traditional ML - classification, regression or clustering problem
Knowledge of API development and microservices using FastAPI, Flask, or Node.js
Strong understanding of MLOps practices, including model monitoring, CI/CD pipelines, experiment tracking, and versioning
Strong debugging and experimentation mindset
Working experience in Software Development Lifecycles, agile methodologies, and continuous integration
Preferred Qualifications
BTech/BE in Computer Science
Experience building real-time inference pipelines using technologies like Kafka, REST APIs, stream processing frameworks
Experience with LLM fine-tuning, prompt engineering, RLHF
Experience building AI-powered agent assist tools, auto-call summaries, and knowledge-grounded responses
Understanding of observability and evaluation frameworks for LLM applications, including prompt evaluation, hallucination detection, and guardrails
Ability to translate ambiguous business problems into scalable AI solutions
