Applied AI Engineer

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.

Benefits

• Build an AI-first cybersecurity platform from the ground up.
• Work at the intersection of AI, cybersecurity, and distributed systems.
• Solve problems where there isn't always an obvious playbook.
• Work directly with founders, researchers, security experts, and engineering leaders.
• Own meaningful problems end-to-end and see your work go directly into

production.

• Be part of an early team where your technical decisions and ideas can have

outsized impact.

• Ship fast, learn fast, and build systems that matter.