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 strong 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 (New Delhi)
🏢 Important Group
📍 New Delhi