AI/ML Engineer

Job Title:

Location

Experience

Role Overview

Mid-Level AI/ML Engineer

Key Responsibilities

1. LLM & Chatbot Integration

   : Build conversational AI using LLMs with context-awareness, domain adaptation, and natural language interaction.

2. Retrieval-Augmented Generation (RAG)

   : Implement vector search with semantic retrieval to ground LLM responses with internal data.

3. Agentic AI

   : Create autonomous agents to execute multi-step actions using APIs, tools, or reasoning chains for automated workflows.

4. Anomaly Detection & UEBA

   : Develop ML models for user behaviour analytics, threat detection, and alert tuning.

5. NLP & Insights Generation

   : Transform user queries into actionable security insights, reports, and policy recommendations.

6. MLOps Ownership

   : Manage end-to-end model lifecycle – training, validation, deployment, monitoring, versioning.

Required Skills

1. Strong Python experience with ML frameworks:

   TensorFlow, PyTorch, scikit-learn

   .
2. Hands-on with

   LLMs

   (OpenAI, HuggingFace, etc.), prompt engineering, fine-tuning, and inference optimization.
3. Experience implementing

   RAG

   using

   FAISS

   ,

   Pinecone

   , or similar.
4. Familiarity with

   LangChain

   ,

   agentic frameworks

   , and multi-agent orchestration.
5. Solid understanding of

   MLOps

   : Docker, CI/CD, deployment on cloud/on-prem infra.
6. Security-conscious development practices and ability to work with structured/unstructured security data.

Preferred

1. Bachelor’s degree in computer science and more preferably Data Science or AI related fields.
2. Experience with cybersecurity use cases: CVE analysis, behaviour analytics, compliance, log processing.
3. Knowledge of open-source LLMs (LLaMA, Mistral, etc.) and cost-efficient deployment methods.
4. Background in chatbots, Rasa, or custom NLP-driven assistants.
5. Exposure to agent tools (LangChain Agents, AutoGPT-style flows) and plugin integration.