About the Role

We are seeking an AI Engineer to setup and manage secure on-premises AI infrastructure, with a strong focus on Security Operations Center (SOC) use cases. The role includes building RAG pipelines, working with vector databases, developing custom models, and creating intelligent tool-integrated chatbots/agents to enhance threat intelligence, incident response, and SOC automation.

Key Responsibilities

- Design, deploy, and maintain on-premises AI infrastructure for hosting large language models.

- Build end-to-end RAG (Retrieval-Augmented Generation) pipelines tailored for SOC workflows.

- Work with vector databases for storing and retrieving security embeddings efficiently.

- Develop and fine-tune custom AI models (including domain-specific fine-tuning using LoRA/QLoRA).

- Build intelligent chatbots and AI agents with tool integration (function calling, API integrations, external tool orchestration).

- Set up high-performance inference servers (vLLM, TensorRT-LLM, Hugging Face TGI, Ollama, etc.).

- Containerize and orchestrate AI workloads using Docker, Kubernetes, and Helm.

- Integrate AI chatbots/agents with SOC tools (SIEM, EDR, ticketing systems, threat intelligence platforms, etc.).

- Optimize models for GPU clusters with quantization, distillation, and performance tuning.

- Ensure security, monitoring, scalability, and compliance of all on-prem AI systems.

- Collaborate with SOC analysts, cybersecurity, and DevOps teams.

Requirements

- Education: Bachelor’s or Master’s in Computer Science, AI/ML, Cybersecurity, or equivalent.





- Strong experience in building RAG pipelines (LangChain, LlamaIndex, Haystack, etc.).

- Practical knowledge of vector databases (Chroma, Weaviate, PGVector, Milvus, Qdrant, FAISS, etc.).

- Experience in developing custom models (fine-tuning open-source LLMs for specific domains).

- Proven experience in building chatbots and AI agents with tool integration (function calling, ReAct agents, LangGraph, tool orchestration, API integrations).

- Robust Python skills with PyTorch and Hugging Face Transformers.

- Hands-on experience with GPU acceleration (CUDA, TensorRT, ONNX) and on-prem deployment.

- Solid knowledge of Docker, Kubernetes, and Linux environments.

Preferred Skills

- Prior work applying AI in SOC / Cybersecurity (threat hunting, log analysis, automated response, knowledge assistants).

- Experience with open-source LLMs (Llama 3, Mistral, Mixtral, Phi, Gemma, Qwen etc.).

- Knowledge of MLOps, CI/CD for AI, and model monitoring.

- Experience with multi-agent systems or advanced agent frameworks.

Qualifications

- Education: Bachelor’s or Master’s in Computer Science, AI/ML, Cybersecurity, or equivalent

Additional Information

Preferred Skills

- Prior work applying AI in SOC / Cybersecurity (threat hunting, log analysis, automated response, knowledge assistants).

- Experience with open-source LLMs (Llama 3, Mistral, Mixtral, Phi, Gemma, Qwen etc.).

- Knowledge of MLOps, CI/CD for AI, and model monitoring.

- Experience with multi-agent systems or advanced agent frameworks.

📌 AI SOC ENGINEER (India)
🏢 SafeAeon
📍 India