Senior AI Engineer

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 [email protected] . How to Apply Interested? Submit your application along with any supporting information- we can’t wait to hear from you!

About Proofpoint

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.

safeguard the digital world and empower people to work securely and confidently.

Join us in our pursuit to defend data and protect people.

Visionary in future focused problem-solving

Exceptional in execution and impact

Responsibilities

• 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

• We're looking for a Senior AI Engineer to architect and ship production-grade AI systems end-to-end.
• You'll own the design of complex agentic AI systems, multi-model pipelines, and intelligent workflow automation — from technical architecture through production deployment and continuous iteration.
• This is a dee p technical role: you write production code, make system-level design decisions, define engineering standards, and mentor junior engineers, while partnering directly with business leaders to turn ambiguous problems into scalable solutions.
• Architect and build production-grade AI-powered applications and agentic systems end-to-end, including front-end, back-end, data pipelines, APIs, and multi-model AI integrations. - Design and operate agentic AI systems — including multi-agent orchestration, tool/function calling pipelines, memory and context management strategies, and dynamic intent routing — with a strong grasp of how these systems behave and fail in production. - Own the full lifecycle of complex AI systems: requirements, architecture design, implementation, evaluation harness construction, deployment, observability, and post-launch iteration. - Drive technical decision-making on model selection, retrieval architectures (RAG, vector search, hybrid), prompt engineering strategies, and LLM API integrations — with clear communication of tradeoffs to engineering and business stakeholders. - Design for scale and reliability: apply software engineering fundamentals including system design principles, data structures, algorithms, and distributed systems thinking to AI application architecture. - Build evaluation and observability infrastructure — including automated eval harnesses, LLM-as-judge pipelines, latency/cost monitoring, and regression frameworks — to continuously measure and improve system quality. - Define and maintain engineering standards for AI development: reusable patterns, integration abstractions, documentation, and testing practices adopted across the team. - Mentor and technically guide junior engineers; conduct code and design reviews, and actively raise the technical bar of the team.

Requirements

• 6+ years of hands-on software engineering experience, with at least 3 years building and shipping production AI/ML systems with real users and measurable business impact. - Dee p proficiency in Go, Python and strong familiarity with typed, production-quality codebases; solid command of data structures, algorithms, and system design principles — capable of designing scalable services, handling concurrency, and reasoning about performance and failure modes. - Hands-on experience building agentic AI systems — including multi-step reasoning pipelines, tool/function calling, dynamic planning, and agent orchestration frameworks (e.g. LangGraph, AutoGen, CrewAI, or custom implementations). - Working knowledge of the MCP (Model Context Protocol) or comparable tool-calling and context injection protocols — including how context windows are managed, how tools are registered and invoked, and how to architect reliable agent-tool interactions. - Strong experience with memory and context management in LLM systems: short-term context strategies, long-term memory via vector stores or knowledge graphs, conversation summarisation, and retrieval-augmented generation (RAG) architectures. - Experience designing and building evaluation harnesses for AI systems: automated test suites, LLM-as-judge patterns, benchmark construction, and production monitoring for quality, latency, and cost. - Production experience with LLM APIs (OpenAI, Anthropic, or equivalent), including prompt engineering, structured output, fine-tuning tradeoffs, and multi-model orchestration.
• Why Proofpoint?

Benefits

• 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 is rooted in values that inspire belonging, empower purpose and drive success-every day, for everyone.