Machine Learning Engineer, Agentic AI- Worldwide Product Marketing

Apple • Cupertino, CA

7d

About The Position

Design and build AI/ML systems, from classical predictive models to LLM-powered agentic applications, to drive business outcomes at scale. Take solutions from research and experimentation through to robust, production-grade deployment, with a growing focus on autonomous agents that reason, plan, use tools, and act on behalf of users and business processes. Work cross-functionally to identify high-value agentic use cases and develop reliable, safe, and measurable products.

The description also characterizes the role as an MLOps Engineer responsible for end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. Build, deploy, and optimize AI/ML applications, emphasizing scalable, production-ready systems, and establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.

Requirements

• At least 4 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
• Proficiency in Python and hands-on experience building distributed systems.
• Experience building large-scale machine learning systems using big data technologies such as Spark, SQL, Snowflake, or similar platforms.
• Solid understanding of ML fundamentals, including supervised and unsupervised learning, model evaluation, and feature engineering.
• Experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, or Hugging Face Transformers.
• Experience building and deploying applications on large language models via APIs or self-hosted inference, such as Claude, GPT, Gemini, Llama, vLLM, or TGI.
• Hands-on experience building agentic systems involving tool or function calling, multi-step reasoning, memory, and RAG, using frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or similar.
• Experience evaluating LLM and agent outputs, including quality assessment, hallucination detection, task-success metrics, and latency benchmarking in production.
• Strong problem-solving skills and ability to translate ambiguous business problems into well-defined ML and agentic solutions.
• Excellent cross-functional communication skills and ability to collaborate effectively across engineering and data science teams.

Nice To Haves

• Familiarity with MLOps practices, including model versioning, CI/CD pipelines, and experiment-tracking tools such as MLflow or similar.

Responsibilities

• Design and build AI/ML systems, including predictive models and LLM-powered agentic applications.
• Take solutions from research and experimentation through production-grade deployment.
• Identify high-value agentic use cases with cross-functional partners and turn them into reliable, safe, and measurable products.
• Drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling.
• Build, deploy, and optimize scalable, production-ready AI/ML applications.
• Establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed