SecNinjaz Technologies LLP

Openings: 1 | Experience: around 2 years

About the role

Build the software that enables AI agents to carry out cybersecurity workflows reliably: orchestration, tool integration, context, state, execution controls and model deployment.

You will develop and test AI systems in Python, working with cybersecurity specialists and a data/model engineer. You need strong applied AI engineering and practical cybersecurity ability to understand security tasks, interpret evidence and diagnose unsuccessful runs.

What you will do

- Build LLM applications and agent workflows using APIs, retrieval, structured outputs and tool calling.

- Develop the agent runtime: task orchestration, persistent state and context, retries, recovery,

cancellation and execution traces.

- Integrate security tools and enforce permissions, approval steps, scope boundaries, execution limits and stop controls in application code. Handle credentials, untrusted target/tool content and isolated execution safely.

- Integrate hosted and open-weight models while preserving application-owned state, evidence and tool contracts. Adapt prompts and integrations where needed and test for regressions.

- Capture task context, tool actions/results, failures, versions, reviewer corrections, timing and cost in a form the data/model engineer can use for reviewed datasets and evaluations.

- Jointly define versioned trace and tool interfaces, evaluation criteria and release checks with the data/model engineer and cybersecurity specialists. Security specialists review domain labels and validate findings.

- Support observable,



recoverable workloads and deployment, including private or self-hosted environments; investigate concurrency, reliability and resource-use problems with the engineering team.

- Work with security specialists and the data/model engineer to test task success, evidence quality, false positives, latency and cost, and release improvements with regression checks and rollback.

SecNinjaz Technologies LLP 1What you should bring

- Around two years of hands-on development experience; solid Python, APIs, Git, debugging and automated testing.

- A working AI/LLM application or agent with tool integration, state, meaningful tests and failure handling.

Be ready to explain, modify and debug your own contribution.

- Practical understanding of model limitations, prompting, structured outputs, retrieval and evaluation.

- Conceptual understanding of training versus inference and of reinforcement-learning fundamentals:

environments, actions, rewards and evaluation. An implemented RL project is welcome but not required for this role.

- Demonstrated practical cybersecurity ability: reason about authentication, authorization, trust boundaries and common web/API or code weaknesses; interpret tool output, reproduce and validate a finding,



reject a false positive and verify a fix in a controlled environment.

- Comfort with Linux, HTTP/APIs, logs and application deployment, and explaining technical decisions to teammates.

Relevant professional work, labs, research and personal projects can demonstrate these skills.

Good to have

- Docker, CI/CD, queues, monitoring or production reliability experience.

- Private inference, open-weight model serving or inference performance work.

- Deeper security-tool integration, detection engineering or security research.

- Fine-tuning or implemented RL experiments.

Initial outcomes, with the team

- Deliver a tested security workflow with useful traces and independently validated outcomes.

- Demonstrate recovery from a failed run and a control, enforced in application code, that blocks an unapproved action.

- Run the workflow through two model integrations with retained application state and regression checks;

supply usable trace records to the data/evaluation pipeline.

Selection process A project discussion and a short practical exercise using a supplied lab or sanitized security case. You will interpret tool outputs that include a false positive, diagnose a failed agent run, implement a recovery or permission-control improvement, and define a trustworthy success check.

AI tools may be used; explain your contribution and be ready to debug your solution. Nothing is run against real systems.

All security work at SecNinjaz is authorized and operates under agreed rules of engagement.

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📌 AI Engineer — Agent & Runtime Engineering (Vijayawada)
🏢 SecNinjaz Technologies
📍 Vijayawada