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Job Description
Role Summary:
We are seeking a Staff AI Engineer to help build next-generation cybersecurity AI products for enterprise security teams.
This is a hands-on individual contributor role for an engineer who combines strong AI engineering capability, practical software development skills, and a strong interest in cybersecurity. You will report to the Director, Cybersecurity AI Product and work as part of a newly formed product development team focused on building and operating agentic solutions that help users investigate threats, triage alerts, automate security workflows, analyze vulnerabilities, and make better security decisions.
The ideal candidate is AI-native, product-minded, proactive, and comfortable working through ambiguity. You should be able to learn quickly, experiment with new AI technologies, build reliable product features, and translate customer problems into practical AI-driven solutions.
This role requires hands-on experience with LLM applications, agentic AI systems, retrieval-augmented generation, tool orchestration, context management, model evaluation, cloud-native software engineering, APIs, and secure software development. Familiarity with cybersecurity concepts, security operations, cloud security, or threat intelligence is strongly preferred.
Key Responsibilities:
- Design and Build Advanced Cybersecurity AI Products
- Build agentic AI workflows for various cybersecurity operations, engineering, and risk processes.
- Convert ambiguous product ideas and early prototypes into secure, maintainable, production-grade AI systems
- Take end-to-end Technical Ownership of Complex AI Product Components, from problem framing to production operation.
- Break down ambiguous problems into technical plans, milestones, risks, and deliverables
- Contribute to technical roadmaps and influence prioritization through evidence, customer feedback, and engineering principles
- Identify design gaps, tech-debts, scalability risks, and make sound engineering trade-offs.
- Research, Evaluate, and Operationalized Emerging AI Technologies
- Continuously Learn, Research and Evaluate Emerging AI technologies
- Translate research findings into practical implementation patterns, product features, internal recommendations, or technical standards
- Share findings through technical documentation, demos, internal talks or fast POCs
- Build Production-Grade AI Products
- Develop backend services, APIs, integrations, AI orchestration, data pipelines, and other software components.
- Support deployment, monitoring, debugging, and operational support of AI products
- Work with CI/CD pipelines, containers, infrastructure as code, logging, metrics, and automated testing
- Design systems that scale across customers, workloads, data sources, and platforms
- Influence Through Technical Excellence
- Provide technical guidance to other engineers through code reviews, design discussions, documentation, and example implementations
- Mentor less experienced engineers informally through strong engineering practice and clear reasoning
- Raise the quality of team decisions by bringing evidence, research, customer context, and practical trade-off analysis
- Communicate complex AI, security, and architecture concepts clearly to technical and non-technical stakeholders
- Collaborate with product, security, solution engineering, and customer-facing teams to ensure product capabilities address real customer workflows
- Demonstrate ownership, proactiveness, customer obsession, and ability to operate effectively in ambiguity
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.
Qualifications
Required Qualifications:
Experience
- 5+ years of professional experience in AI engineering and software engineering.
- 3+ years of hands-on experience building AI/ML, LLM-based, automation, data-driven, or intelligent software systems.
- Experience building production software, not only prototypes or notebooks.
- Experience working in product engineering teams or delivering customer-facing technical solutions.
AI Engineering:
Strong practical experience with LLM applications, AI workflow automation, AI assistants, copilots, or agentic AI systems.
Familiarity with concepts such as:
- Prompt engineering
- System prompts and instruction design
- Retrieval-augmented generation
- Vector search
- Embeddings
- Tool calling
- Function orchestration
- Context management
- Agent memory
- Evaluation datasets
- Model benchmarking
- Feedback loops
Experience with agentic AI frameworks or tools such as:
- LangChain
- LlamaIndex
- Semantic Kernel
- AutoGen
- CrewAI
- Vector databases
- Knowledge graphs
- Model evaluation frameworks
Ability to evaluate AI outputs objectively and improve quality, safety, reliability, latency, and cost.
Experience using commercial or open-source AI models and APIs.
Demonstrated ability to use AI tools in everyday work for coding, debugging, research, documentation, testing, or productivity improvement.
Software Engineering:
Strong programming ability in two or more languages such as:
- Python
- TypeScript / Node.js
- Go
- Java
- Rust
Strong understanding of software architecture, API design, data modeling, distributed systems, asynchronous processing, observability, and maintainability
Experience building backend services, APIs, data pipelines, integrations, or cloud-native applications.
Familiarity with web applications, distributed systems, databases, CI/CD pipelines, logging, monitoring, and automated testing.
Ability to write clean, maintainable, secure, and production-ready code.
Experience participating in design reviews, code reviews, and production troubleshooting.
Cybersecurity and Secure AI Awareness:
- Working knowledge of cybersecurity principles, secure software development, and common vulnerabilities.
- Ability to assess security implications of AI-enabled workflows and software architecture decisions.
- Interest and ability to deepen cybersecurity domain expertise quickly.
- Awareness of AI-specific security risks, including prompt injection, data leakage, hallucination, adversarial inputs, unsafe tool execution, and excessive permissions.
Cloud and Infrastructure:
- Experience developing or deploying applications on cloud platforms such as AWS and/or Microsoft Azure.
- Familiarity with containers, serverless services, managed databases, event-driven systems, queues, object storage, and cloud identity concepts.
- Understanding of observability, deployment automation, environment configuration, and operational reliability.
- Awareness of cloud security principles, IAM, secrets management, and secure configuration.
Leadership Qualities:
- Strong ownership mindset and accountability for outcomes.
- Proactive approach to identifying problems and proposing solutions.
- Customer-obsessed mindset with focus on practical user value.
- Comfortable dealing with ambiguity and evolving requirements.
- Strong analytical and problem-solving skills.
- Clear written and verbal communication skills.
- Strong habit of continuous learning, experimentation, and knowledge sharing.
- Ability to work independently and collaborate effectively in a small, fast-moving team.
Preferred Qualifications:
- Advanced degree (Master’s degree or Ph.D.) in computer science or related fields
- Experience building AI agents, LLM applications, copilots, automation systems, or RAG-based products
- Experience with cybersecurity products, security automation, SOC tools, SIEM/SOAR integrations, cloud security platforms, identity security, threat intelligence, or incident response workflows
- Familiarity with AI security risks, including prompt injection, data leakage, hallucination, unsafe tool use, and adversarial inputs
- Experience with evaluation methods for LLM systems, including golden datasets, human review workflows, offline evaluation, online evaluation, and regression testing
- Experience with Kubernetes, infrastructure as code, observability platforms, secrets management, or cloud-native security
- Familiarity with security frameworks such as MITRE ATT&CK, OWASP, NIST Cybersecurity Framework, CIS Controls, or ISO 27001
- Experience working in early-stage product teams, startups, innovation teams, or ambiguous product incubation environments.
Visa is an EEO Employer
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.