Machine Learning Engineer, Agentic AI- Worldwide Product Marketing

As a Machine Learning Engineer, you will design and build AI/ML systems - from classical predictive models to LLM-powered agentic applications - that drive meaningful business outcomes at scale. You will 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. You will work cross-functionally to identify high-value agentic use cases and turn them into reliable, safe, and measurable products. The MLOps Engineer will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. This hire will build, deploy, and optimize AI/ML based applications with a strong emphasis on scalable, and production-ready systems. You will establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.Collaborate with other MLEs to build scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment Design end-to-end AI/ML solutions with clear business impact, with a strong focus on feasibility, scalability, and performance Benchmark, adapt, and integrate ML and foundation models into existing systems Conduct rigorous model evaluation, testing, and iteration to continuously improve model quality and efficiency Design, build, and deploy LLM-powered agents and multi-agent systems that plan, reason over multiple steps, and complete complex business workflows with human-in-the-loop oversight Apply prompt and context engineering, structured outputs, and model routing to balance quality, latency, and cost across agent workflows Integrate classical ML models (e.g., forecasting, classification, ranking) as tools within agent workflows to ground agent decisions in quantitative predictions Deploy, monitor, and support agents and models in production with end-to-end tracing and observability (per-step

Machine Learning, Artificial Intelligence (AI), International Marketing, Machine Learning, Business Processes, Use Cases, Communication Skills, Artificial Intelligence (AI), Engineering, Application Programming Interface (API), traces, token usage, failure modes), feeding insights back into iteration Partner with data scientists, engineers, and business stakeholders to identify high-value agentic use cases and translate business requirements into technical ML solutions Contribute to shared ML and agent infrastructure, reusable components, tooling, and best practices across teams4+ years of related 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 (e.g., Claude, GPT, Gemini, Llama, or other open-source models) via APIs or self-hosted inference (e.g., vLLM, TGI) Hands-on experience building agentic systems - tool/function calling, multi-step reasoning, memory, and RAG - with 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 with the ability to translate ambiguous business problems into well-defined ML and agentic solutions Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teamsFamiliarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow or similar