Focus: Backend Architecture & Agentic Systems

Company: SecNinjaz Technologies LLP

Employment Type: Full-Time

Experience: Minimum 5+ years in software development or backend engineering, including hands-on experience developing AI applications.

Locations:

- Ratan Tata Innovation Hub, Mangalagiri, Vijayawada, Andhra Pradesh

Work Mode: Strictly on-site at either location. Remote and hybrid arrangements are not available.

About SecNinjaz

SecNinjaz Technologies LLP is an ISO 27001-certified cybersecurity and technology company serving government, enterprise, fintech, healthcare and strategic sectors. Our work spans VAPT, security operations, Zero Trust Architecture, cloud security, secure communication and IT engineering.

We are expanding our AI engineering team to develop intelligent applications, agentic workflows and AI-augmented cybersecurity products.

Role Overview

We are looking for a hands-on Technical Lead who can design the backend architecture of AI applications, implement critical components and guide engineers from requirements through production deployment.

The primary requirement is strong backend and system architecture expertise combined with practical experience in AI application development. The candidate should understand how APIs, databases, orchestration services, agent runtimes, model endpoints and infrastructure work together as a reliable product.

You will lead technical delivery, mentor the engineering team and collaborate with cybersecurity researchers, ML engineers, DevOps and product stakeholders. Cybersecurity knowledge, advanced ML experience and familiarity with GitLab, CI/CD and sprint management are preferred strengths.

Key ResponsibilitiesBackend Architecture and System Design

- Own High-Level Design (HLD) and Low-Level Design (LLD), including service boundaries, data flows, API contracts, database schemas and deployment architecture.
- Design and develop backend services for AI applications, agent execution, knowledge retrieval, integrations and user workflows.
- Select appropriate architecture and technologies based on product requirements, scale, security and maintainability.
- Build reliable asynchronous processing using queues, background workers and persistent workflow state.
- Design for retries, idempotency, timeouts, cancellation, recovery and concurrent workloads.
- Conduct architecture and code reviews, document technical decisions and resolve performance bottlenecks.

Agentic AI and Orchestration
- Design and implement agentic workflows involving planning, tool calling, context management, memory and structured outputs.




- Develop orchestration across agents, backend services, models and external tools.
- Build reusable agent skills with defined inputs, outputs, permissions, error handling and automated tests.
- Implement retrieval-augmented generation (RAG), knowledge-base integration and context selection where required.
- Establish execution limits, checkpoints, approval steps and traceability for sensitive workflows.
- Evaluate agent reliability, task completion, output quality, latency and resource consumption.

AI Infrastructure and Production Delivery
- Integrate hosted model APIs and self-hosted model endpoints through maintainable interfaces.
- Work with infrastructure and DevOps teams on deployment, environment configuration, model serving and compute requirements.
- Plan for concurrency, inference latency, GPU resources and operating costs where relevant.
- Establish application monitoring, structured logs, distributed tracing and failure diagnosis.
- Support repeatable releases, rollback, production troubleshooting and ongoing reliability improvements.

Technical Leadership and Engineering Execution
- Translate product requirements into technical plans, development tasks and clear acceptance criteria.
- Lead and mentor backend and AI engineers while contributing directly to implementation.
- Coordinate dependencies across AI, cybersecurity, DevOps, QA and product teams.
- Support sprint planning, estimation, backlog refinement, technical reviews and delivery tracking.
- Establish development standards covering version control, documentation, testing and code review.
- Work with the team to strengthen GitLab workflows and CI/CD pipelines, including automated quality checks and controlled releases.

Secure AI Application Development
- Implement secure APIs, authentication, role-based access control, secrets management and audit logging.
- Design appropriate separation between customer data, agent execution environments and infrastructure credentials.
- Collaborate with cybersecurity researchers to translate validated research methods into reliable product workflows.
- Support AI-augmented VAPT and security automation use cases with reproducible evidence, human review and remediation tracking.




- Incorporate safeguards against prompt injection, unsafe tool execution and unintended data exposure.

Mandatory Requirements
- Minimum 5+ years of relevant software development or backend engineering experience.
- Strong hands-on experience designing and building production backend systems.
- Practical experience developing and deploying AI or LLM applications beyond prototypes.
- Ability to produce and explain HLD, LLD, API designs, database designs and deployment plans.
- Strong Python programming skills and experience with a production backend framework such as FastAPI, Django or an equivalent.
- Working experience with databases, caching, queues, background processing and application integrations.
- Practical understanding of agentic AI, orchestration, tool calling, structured outputs and RAG workflows.
- Demonstrated technical ownership, code review, mentoring and problem-solving capability.
- Ability to communicate architecture decisions and delivery risks clearly.
- Willingness to work full-time on-site at either of the specified locations.

Preferred Qualifications
- Experience in cybersecurity, VAPT, security automation, SOC platforms or secure enterprise applications.
- Experience managing sprint execution, engineering backlogs and cross-functional delivery.
- Familiarity with GitLab, merge requests, branching strategies and CI/CD pipelines.
- Experience with Docker, Kubernetes, Linux, cloud infrastructure or on-premises deployments.
- Knowledge of ML, model evaluation, fine-tuning, dataset quality and model improvement.
- Experience with PyTorch, TensorFlow, scikit-learn or relevant MLOps tools.
- Experience with GPU-based inference, open-weight models, vector databases or restricted-network environments.
- Familiarity with agent frameworks and integration protocols such as LangGraph or MCP.

Expected Outcomes
- A documented backend architecture that engineers can implement and maintain.
- Reliable AI workflows with measurable quality, explicit execution controls and production visibility.
- A team that delivers tested features through consistent engineering practices.
- An extensible foundation for SecNinjaz’s AI applications and cybersecurity products.

Application Details Please submit your resume and a brief summary of relevant production projects, highlighting your personal contribution to backend architecture, AI workflows, deployment and team leadership.
- Include your preferred work location— Mangalagiri, Vijayawada—and your notice period. Relevant architecture samples or project links may also be included.

📌 Technical Lead – AI Development (Vijayawada)
🏢 SecNinjaz Technologies
📍 Vijayawada