Machine Learning Engineer, Agentic AI- Worldwide Product Marketing
Marketing & Communications, Software Engineering, Product, Data Science
USD 184,700-277,600 / year + Equity
Posted on Oct 30, 2026
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 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 teams
- 4+ 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 teams
- Familiarity with MLOps practices including model versioning, CI/CD pipelines, and experiment tracking tools such as MLflow or similar