AI / ML Engineer

AI Engineer

Generative AI & Intelligent Systems

The Role

SecNinjaz builds AI-driven solutions for enterprise, government and mission-critical environments. We are hiring AI Engineers (3+ years) to design, build and ship production-grade intelligent systems using Generative AI, LLMs, RAG and AI agents. Beyond the models, you should be able to build the full product around them - knowledge systems, backend, deployment, security and architecture.

What You'll Do

▸ Build production-grade Generative AI applications and RAG systems over enterprise knowledge bases.

▸ Develop AI agents, tool-calling and multi-step reasoning workflows.

▸ Deploy open-weight and local LLMs for secure, on-premise use.

▸ Design end-to-end architecture: ingestion, embedding, retrieval, reranking, reasoning, tools and validation.

▸ Write secure Python backend services and APIs, and integrate with databases and external tools.

▸ Evaluate models for accuracy, latency, cost and security; improve reliability with grounding, guardrails and hallucination reduction.

▸ Partner with cybersecurity teams on intelligent security and automation, and contribute to R&D and architecture decisions.

Core Technical Requirements

Generative AI & LLMs

▸ Hands-on with LLMs, embeddings, context engineering and RAG (semantic/hybrid retrieval, metadata filtering, reranking).

▸ AI agents, tool/function calling and multi-step reasoning; local/open-weight deployment and model evaluation.

▸ Fine-tuning, LoRA/QLoRA or domain adaptation is a plus.

Frameworks & Platforms

▸ PyTorch and Hugging Face Transformers.

▸ Orchestration: LangGraph, LangChain, LlamaIndex or equivalent.

▸ Serving: vLLM, Ollama, llama.cpp or equivalent.

▸ Vector/search: Qdrant, Milvus, Weaviate, pgvector, Elasticsearch/OpenSearch or FAISS.

Backend & Infrastructure

▸ Strong Python and API development; FastAPI or similar.

▸ SQL, PostgreSQL, Redis; Linux, Docker, Git and GPU environments.

▸ Kubernetes, cloud, MLOps/LLMOps and distributed inference are advantageous.

System Architecture

You should be able to design how the pieces of an AI product fit together, and reason about scalability, latency, reliability, GPU use, privacy and security:

Cybersecurity - Strongly Preferred

Security knowledge is strongly preferred, especially for intelligent security products: VAPT, web/API security, network or cloud security, OWASP/CVE/CWE/CVSS, security automation, and AI/LLM security or red teaming. Candidates combining AI and cybersecurity get strong preference.

Computer Vision - A Plus

Not mandatory, but a plus for multimodal and video work: object detection/tracking and video analytics, OpenCV, YOLO-family models, Vision Transformers, real-time GPU inference, and Vision Language Models (VLMs).

Education & Experience

Minimum 3+ years in AI/ML, Generative AI, LLM engineering or AI product development. We prefer people who have taken at least one AI system from prototype through architecture, deployment and production. Degree in AI, ML, CS, Data Science, Cybersecurity or a related field (B.Tech/B.E./M.Tech/M.E./MS/MCA); exposure to both AI and cybersecurity is highly preferred.

What We Look For

▸ Strong problem-solving and system design; able to research, prototype and productise independently.

▸ Good judgement on when to use prompting, RAG, fine-tuning, agents, tools or traditional ML.

▸ Real systems beyond basic chatbot/API integrations; comfort across AI, security, dev and infra teams.

▸ Interest in secure, sovereign, enterprise-grade AI.