What You'll Do
Build multi-agent systems that enable collaboration between agents and tools—implementing communication patterns, memory structures, and orchestration logic so agents can reason, plan, and act effectively.

Use Generative AI to augment human and system intelligence, applying LLMs to automate data synthesis, decision support, and workflow optimization.
Implement context engineering solutions to retrieve relevant information from internal data, MCP tool calls, and prior interactions—constructing focused contexts that improve reasoning accuracy.
Design and implement evaluation frameworks to measure stochastic outcomes, providing visibility into how prompts, models, and agent architectures impact user value and reliability.
Work with datasets—explore patterns, enrich or transform raw inputs, and apply generative or predictive methods to enable new product capabilities.
Prototype and iterate on new agentic patterns—moving from idea to measurable impact through experimentation, A/B testing, and telemetry-driven improvement.
Translate experimental models and reasoning patterns into scalable, observable, and cost-efficient production services.
Collaborate with PMs, designers, and engineers to deliver product outcomes—identifying customer pain points and implementing AI-first solutions.
Contribute to the Agentic Platform SDK, implementing abstractions and APIs that enable teams to compose, evaluate, and deploy intelligent agents.

What You'll Bring

Strong Python skills—able to prototype quickly and deliver maintainable, production-grade systems.
Solid ability to explore datasets, form hypotheses, and execute projects that leverage data through enrichment, transformation, or generative AI techniques.
Experience building LLM-powered and agentic systems using frameworks such as LangChain, LangGraph, or custom orchestration layers.
Understanding of context retrieval, grounding, and compression techniques to optimize reasoning performance.
Experience quantifying stochastic performance and designing evaluation metrics that measure prompt quality, reasoning depth, and user-perceived value.
Familiarity with vector databases, embedding stores, and retrieval-augmented generation (RAG) architectures.
Experience with cloud-native systems (AWS/GCP), containerization (Docker/Kubernetes), and modern observability tooling.
A product-focused mindset—thinking in terms of user value and thriving in environments where specifications evolve and goals are outcome-driven.
Experience contributing to platforms and reusable components that enable other teams to build AI-driven solutions.
Strong communication skills, collaborative approach, and ownership mindset in complex, fast-moving projects.

The US base salary range for this position is $XXX,XXX - $XXX,XXX + variable compensation + benefits.

Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process.

We want our employees and their families to thrive. In addition to comprehensive benefits we offer holistic mind, body and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here.

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