AI / ML Engineer

AI & Intelligence Team · SecNinjaz Technologies LLP

Position Details

Role AI / ML Engineer

Team AI & Intelligence

Location Delhi, India (Netaji Subhash Place) — on-site with hybrid flexibility

Employment Full-time, permanent

Experience 1 – 4 years of hands-on AI/ML delivery

Compensation As per industry standards, commensurate with experience

About SecNinjaz

SecNinjaz Technologies LLP is a Delhi-headquartered AI-native cybersecurity and technology firm serving enterprises, governments, and intelligence customers. Our AI portfolio delivers AI-native services and sovereign products across multiple verticals: advanced AI agents, agentic workflow automation, agentic AI development, AI/LLM security testing, AI red-teaming, AI SOC automation, AI integration, and applied AI for imagery, language, and OSINT.

Founded in 2018. Four in-house AI products anchor our work: NAINA (imagery narrative analytics), Kaalix AI (AI-augmented VAPT), WebMine (AI-powered OSINT across 250+ sources), and textr (post-quantum encrypted messenger). Certifications held: ISO/IEC 42001:2023 (AI Management System), ISO/IEC 27001:2022, ISO 9001:2015, ISO/IEC 20000-1:2018, ISO/IEC 27701:2025, and CMMI Level 3.

The Role

This is a hands-on engineering role. You will design, build, and productionise ML and LLM systems that power our products and internal automation. You will own components end to end — problem framing, dataset work, evaluation, deployment, and monitoring — often in environments that require on-prem, air-gapped, or otherwise sovereign deployment. Close collaboration with security engineers, product engineers, and the GRC team is the norm, not an exception.

SecNinjaz separates AI-augmented from AI-native and does not overclaim. That standard applies inside the team as well: measured, not claimed.

Key Responsibilities

- Design and ship production ML, LLM, and multi-agent systems across Kaalix AI, WebMine, NAINA, and internal AI automation.





- Build advanced AI agents — planning, tool use, multi-agent orchestration, and evaluation loops — using LangGraph, CrewAI, AutoGen, or equivalents.

- Deliver across AI verticals: AI/LLM security testing, AI red-teaming, AI SOC automation, agentic workflow automation, agentic AI development, and AI integration.

- Build RAG and hybrid retrieval pipelines that hold up under adversarial and out-of-distribution inputs.

- Fine-tune, distil, or adapt open-weight models for sovereign, on-prem, or air-gapped deployments where API-only options are not acceptable.

- Design evaluation harnesses, red-teaming suites, and drift monitoring for AI systems; report performance in numbers, with error bars, not adjectives.

- Own the MLOps around your work: reproducible training, versioned artefacts, containerised inference, observability, and rollback.

- Contribute to AI governance aligned to ISO/IEC 42001 (AI Management System) and DPDP Act 2023.

- Mentor interns and junior AI engineers, review pull requests, and write internal AI documentation that outlives the person who wrote it.

Must Have

- M.Tech in Computer Science, AI/ML, or a related discipline.

- 1 – 4 years of hands-on experience delivering ML or AI systems to production; independent work and solid internships count if the outcome shipped and you can defend the design.

- Strong Python; solid working knowledge of PyTorch (preferred) or TensorFlow.

- Real experience building LLM applications and AI agents: prompt design, RAG, fine-tuning, tool use, and agentic frameworks (LangChain, LlamaIndex, LangGraph, CrewAI,



AutoGen, or equivalents).

- Working knowledge of vector databases (FAISS, pgvector, Weaviate, or Milvus) and rigorous evaluation and benchmarking techniques for AI systems.

- MLOps fundamentals: Docker, model registries, CI/CD, and at least one orchestrator (Kubernetes, Airflow, or similar).

- Comfortable in Linux, with REST/gRPC APIs, and with distributed-systems basics.

- Clear written and spoken English; the ability to explain AI trade-offs to non-AI stakeholders and to write documentation that engineers actually read.

Nice to Have

- Multi-agent orchestration experience — planner-worker, supervisor-subordinate, or graph-based agent systems in production.

- Applied AI in security — AI red-teaming, LLM security testing, AI SOC automation, or AI-augmented VAPT.

- Computer vision experience (object detection, tracking, multi-modal models) relevant to NAINA.

- On-prem, air-gapped, or edge deployment experience for AI workloads.

- Familiarity with AI safety, adversarial ML, prompt injection defence, or model watermarking.

- Model-serving optimisation: quantisation, GGUF/ONNX, vLLM, TGI, or Triton.

- Open-source contributions or published research in AI/ML.

Why SecNinjaz

- Real ownership. Small, senior team. You decide how a system gets built, and you are accountable for how it performs in production.

- Sovereign work. A large share of what we build has to run without a public-cloud dependency. That constraint forces good engineering.

- AI grounded in reality. We separate AI-augmented from AI-native and refuse to overclaim. That discipline applies internally too.

- Audited process. ISO/IEC 27001, 9001, 20000-1, 27701, 42001 and CMMI Level 3 mean the processes around your work are audited, not folklore.

- Domain edge. You will work at the seam of cybersecurity and AI — a rare combination outside of a few dedicated labs.

📌 AI / ML Engineer (Delhi)
🏢 SecNinjaz Technologies
📍 Delhi