About Simbian
Simbian is building an Agentic AI platform for cybersecurity. Our AI agents automate
security operations and provide customers with 10x leverage across critical security
workflows.
Our initial use cases include:
• AI-based SOC alert triage and investigation
• AI-based Threat Hunting
• AI-based Penetration Testing
Founded by repeat successful security founders, Simbian brings together a strong team of
engineers, security experts, and operators working on some of the hardest problems at the
intersection of cybersecurity and AI.
Our core values are excellence, replication, and intellectual honesty. We believe in
building exceptional technology, sharing what we learn, and being honest about what
works—and what doesn't.
The Role
We are looking for an Applied AI Engineer to help build the systems that make our AI
agents powerful, reliable, and useful in real-world cybersecurity environments.
This role sits at the intersection of backend engineering, distributed systems, and
applied AI.
You will build the backend infrastructure that powers our agents while also working directly
on agent behavior—improving how agents reason, use tools, retrieve context, handle
failures, and complete complex tasks.
The goal isn't simply to build models or backend services in isolation. It's to turn AI
capabilities into dependable production systems that solve real customer problems.
You'll work closely with research, engineering, product, and security teams to take ideas
from experimentation to production and measure whether they improve agent
performance.
Requirements
Applied AI & Agent Systems
• Design and iterate on AI agent behaviors across real-world cybersecurity and
software engineering workflows.
• Build multi-step agent workflows with tool calling, branching logic, retries,
validation, and human-in-the-loop controls.
• Develop tool schemas, execution strategies, context construction, memory, and
retrieval mechanisms that improve agent performance.
• Experiment with prompting, model-facing strategies, tool-use patterns, and context
engineering.
• Analyze agent failures and systematically turn failure modes into product and
engineering improvements.
• Build guardrails, policy layers, and safe-execution mechanisms for agents operating
in security-sensitive environments.
• Help define what "good" looks like for an agent completing complex tasks end-to
end.
Evaluation & Agent Performance
• Design and run evaluations to measure agent quality, reliability, regressions, and
edge cases.
• Build evaluation pipelines, test harnesses, scoring frameworks, and golden
datasets.
• Create feedback loops that bring real-world task data and production failures back
into evaluation and development.
• Analyze production traces and agent behavior to identify opportunities for improving
solve rate, usefulness, and reliability.
• Work with research and engineering teams to translate experimental improvements
into measurable production gains.
Backend & Distributed Systems
• Design and build scalable backend services that power AI agents and cybersecurity
workflows.
• Build high-scale, multi-tenant systems that securely support multiple customer
environments.
• Work with microservices, asynchronous execution, event-driven architectures, and
distributed systems.
• Build ingestion, indexing, retrieval, and agent-memory layers for large volumes of
security data.
• Design reliable execution systems with strong observability, traceability,
monitoring, and data-quality guarantees.
• Build and maintain integrations with enterprise security platforms such as SIEM,
SOAR, EDR, and NDR systems.
• Own features end-to-end—from architecture and implementation through
deployment, monitoring, debugging, and iteration in production.
Product & Cross-functional Collaboration
• Work closely with product, research, infrastructure, and security teams to turn
ambiguous problems into working systems.
• Partner with customer-facing teams to understand real-world failures and improve
the product based on user needs.
• Help shape the interfaces and workflows through which users interact with AI
agents.
• Contribute to architectural decisions and technical direction as the platform
evolves
• Have 5–8 years of software engineering experience, with strong backend
development experience.
• Have experience building and shipping ML/LLM-powered products or AI-enabled
features, or have strong hands-on experience applying LLMs to real engineering
problems.
• Are highly proficient in Python and comfortable working with modern AI/ML tooling.
• Have strong fundamentals in distributed systems, backend architecture, APIs,
microservices, and asynchronous systems.
• Have experience with LLMs, prompt engineering, RAG, embeddings, model
evaluation, or agentic systems.
• Think beyond model metrics and engineering elegance—you care about whether
the system actually works for users.
• Enjoy debugging messy, real-world failures and turning them into systematic
improvements.
• Are comfortable working in ambiguous environments and taking ownership from
problem definition → implementation → production.
• Have a strong understanding of software engineering fundamentals and write clean,
maintainable, well-tested code.
• Enjoy reading technical papers, RFCs, experimenting with new technologies, and
learning quickly.
Must Have
• 4–7 years of professional software engineering experience.
• Strong backend development experience, preferably with Python, Go, or Node.js.
• Strong understanding of distributed systems fundamentals.
• Experience with microservices, APIs, asynchronous execution, and event-driven
systems.
• Hands-on experience with LLMs / Generative AI / Applied AI.
• Experience with at least some of:
o Prompt engineering
o RAG
o Embeddings / vector databases
o LLM evaluation
o Tool calling / function calling
o Agent frameworks
• Experience building and deploying production software.
• Strong problem-solving and debugging skills.
• Ownership mindset and ability to take a problem from zero to shipped.
Bonus / Great to Have
• Experience building AI agents or tool-using LLM systems.
• Experience with LangGraph, LangChain, or similar agent frameworks.
• Experience with model evaluation, fine-tuning, or code-generation models.
• Experience building developer tooling or AI coding systems.
• Experience with cybersecurity products, particularly SIEM, SOAR, EDR, NDR, or
security data pipelines.
• Experience with AWS/GCP/Azure, Docker, Kubernetes, and CI/CD.
• Experience building evaluation frameworks, benchmark datasets, or automated
testing systems for AI agents.
• Experience with agent observability, tracing, and production LLM monitoring.
• Strong academic background in Computer Science or a related field; graduates
from IITs or other top-tier engineering institutions preferred.