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About Us:
Proofpoint is a global leader in human- and agent-centric cybersecurity. We protect how people, data, and AI agents connect across email, cloud, and collaboration tools. Over 80 of the Fortune 100, 10,000 large enterprises, and millions of smaller organizations trust Proofpoint to stop threats, prevent data loss, and build resilience across their people and AI workflows. Our mission is simple: safeguard the digital world and empower people to work securely and confidently. Join us in our pursuit to defend data and protect people.
How We Work:
At Proofpoint you’ll be part of a global team that breaks barriers to redefine cybersecurity guided by our BRAVE core values:
Bold in how we dream and innovate
Responsive to feedback, challenges and opportunities
Accountable for results and best in class outcomes
Visionary in future focused problem-solving
Exceptional in execution and impact
The Role
Proofpoint is hiring an Agentic AI Engineer on the team building our agentic AI platform — the agent runtimes, MCP access layer, and evaluation tooling that product teams across the company build on. You'll be building for other engineers: the people shipping agentic features into products that thousands of enterprises rely on around the clock.
This role sits at the frontier of agentic technology. New models, frameworks and protocols land faster than any single product team can absorb, and part of your job is to absorb them first: work out what actually holds up, establish the best practices, and prove them out in reference agentic applications that other teams can learn from and build on. You get first look at the new stuff, and the fun part of the job is showing engineers across the company what it can do.
What you’ll do
Build reference agentic applications on our platform — end-to-end examples of how agents should be designed, evaluated, deployed and monitored at Proofpoint .
Establish and document best practices: tool design and MCP integration, prompt and context management, memory and planning, cost/latency tradeoffs, failure recovery, and what "release-ready" means for a nondeterministic system.
Design evaluation harnesses — task suites, baselines, and quality, cost and latency metrics — and use them to decide what ships.
Turn ambiguous questions into working prototypes quickly, and present results to the teams who asked; recommend just as readily when an approach should be abandoned.
Harden successful prototypes into reusable platform capabilities that product teams can adopt.
Partner with experienced engineers, security researchers and product tech leads to go deep on the systems you build on.
Socialize agentic patterns across the company through demos, design reviews, written guides, and pairing sessions.
Communicate and understand stakeholders' requirements and participate in cross-team design discussions.
What we’re looking for
Bachelors or Master's Degree in Computer Science or a related field, or equivalent practical experience.
3+ years of engineering experience, with recent hands-on work on LLM or agentic systems that went past the prototype stage.
Good understanding of the agentic development lifecycle and its components - agent runtimes, tool and MCP integration, prompt and context management, memory, planning, orchestration, evals, tracing, guardrails, rollout and rollback.
Solid Python; TypeScript/Node a plus.
Experience working with globally distributed teams .
Experience building and running services in a cloud microservices environment — containers, CI/CD, automated deployment; AWS a plus.
Comfortable diagnosing distributed systems from traces and metrics; OpenTelemetry (including GenAI semantic conventions) a plus.
An eval-first mindset: measurement comes with the build, not after it.
Genuine enthusiasm for frontier AI — you already experiment with agent frameworks and LLM SDKs (LangGraph, Anthropic/OpenAI Agents SDK, MCP, LangChain or similar) and follow where the field is going.
Initiative and a bias toward shipping — you pick up ambiguous problems, get a PoC working and presented in days rather than quarters, and keep moving when the path isn't mapped out.
Fast learner with real curiosity — you want to know how the systems underneath you work, not just how to call them.
Preferred
Use of AI coding tools (Claude Code, Codex or similar) and familiarity with agent interoperability protocols such as MCP and A2A.
Building eval harnesses, benchmarks or ground-truth datasets.
Producing developer-facing reference implementations, SDK samples, or internal enablement material.
AG and semantic retrieval (vector stores, hybrid search), and working with both hosted APIs and open-weight models.
Why Proofpoint ?
At Proofpoint , we believe that an exceptional career experience includes a comprehensive compensation and benefits package. Here are just a few reasons you’ll love working with us:
Competitive compensation
Comprehensive benefits
Career success on your terms
Flexible work environment
Annual wellness and community outreach days
Always on recognition for your contributions
Global collaboration and networking opportunities
Our Culture:
Our culture is rooted in values that inspire belonging, empower purpose and drive success-every day, for everyone.
We encourage applications from individuals of all backgrounds, experiences, and perspectives. If you need accommodation during the application or interview process, please reach out to .
How to Apply
Interested? Submit your application along with any supporting information- we can’t wait to hear from you!