Machine Learning Engineer, Agentic AI, Worldwide Product Marketing

What you'll do

• Design and build end-to-end AI/ML solutions from initial concept through to production-grade deployment.
• Build and deploy LLM-powered agents and multi-agent systems capable of multi-step reasoning and complex workflow completion.
• Apply prompt engineering, context engineering, and model routing to optimize agent workflows for quality, latency, and cost.
• Integrate classical ML models as tools within agent workflows to ground decisions in quantitative predictions.
• Deploy and monitor production models with end-to-end tracing, observability, and failure mode analysis.
• Conduct rigorous model evaluation, testing, and iteration to improve model quality and efficiency.
• Establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.
• Contribute to shared ML infrastructure, reusable components, and best practices across the organization.

What we're looking for

• 4+ years of experience building high-throughput, scalable applications or machine learning models in a production environment.
• Proficiency in Python with 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/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.
• Hands-on experience building agentic systems using frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or similar.
• Familiarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow (preferred).