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Applied AI Engineer
Job Location:
Monthly Salary:
Not provided by the employer
Monthly Salary:
Experience Required:
5years
Experience Required:
Posted:
11 August 2026 (30+ days ago)
Posted:
Application Deadline:
8 November 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
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 worksand what doesnt.
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 behaviorimproving how agents reason use tools retrieve context handle failures and complete complex tasks. The goal isnt simply to build models or backend services in isolation. Its to turn AI capabilities into dependable production systems that solve real customer problems. Youll 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-endfrom 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 58 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 eleganceyou 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
47 years of professional software engineering experience.
Strong backend development experience preferably with Python Go or .
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 databaseso LLM evaluation o Tool calling / function callingo 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 isnt 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.
Required Skills:
58 years of professional software engineering experience. Strong backend development experience preferably with Python Go or . 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.
