AI/ML Engineer

codingcircle; Bengaluru; On-site; INR 600,000 - 1,200,000; Full time

Job summary

Secpod is looking for an AI/ML Engineer to develop and implement machine learning models focused on cybersecurity. The role requires a Bachelor’s or Master’s degree in Computer Science or Data Science, proficiency in Python, and a strong understanding of ML concepts and tools like TensorFlow and PyTorch. As an AI/ML Engineer, you will engage in model lifecycle management, including preprocessing, training, and optimization, in collaboration with cross-functional teams. This position is full-time based in Bengaluru, Karnataka.

Qualifications

• Bachelor’s or Master’s degree in computer science, Data Science or a related field.
• Strong understanding of Transformers and core ML concepts and algorithms.
• Proficiency in Python and ML libraries/frameworks.

Responsibilities

• Design and develop ML and deep learning models for cybersecurity.
• Implement supervised, unsupervised, and semi-supervised learning techniques.
• Fine-tune LLMs and build RAG pipelines for cybersecurity tasks.

Skills

Transformers

Machine Learning concepts and algorithms

Python

Scikit-learn

TensorFlow

PyTorch

Hugging Face Transformers

MLOps tools

Education

Bachelor’s or Master’s degree in Computer Science or Data Science

Tools

MLflow

DVC

Docker

FastAPI

Job description

Job Description: AI/ML Engineer

SecPod is a cybersecurity technology company.

Company : Secpod

Job Type : Full-Time

Experience : 0-2 Years (Data Science and AI/ML)

Location : Bengaluru

Responsibilities:

• AI/ML Model Development : Design and develop classical ML and deep learning models to predict, detect, and prevent cyber threats. Apply models to real-world cybersecurity datasets.
• ML Fundamentals : Implement supervised, unsupervised, and semi-supervised learning techniques such as regression, classification, clustering, anomaly detection, and ensemble methods.
• LLM Fine-Tuning & RAG Architecture : Fine-tune Large Language Models (LLMs) and build RAG (Retrieval-Augmented Generation) pipelines for tasks such as threat summarization, document understanding, and contextual search.
• Vector Database Integration : Work with vector databases (FAISS, Pinecone, Weaviate, etc.) to build high-performance semantic search solutions.
• Data Analysis & Feature Engineering : Analyze cybersecurity logs, vulnerability reports, and event data to engineer features and extract intelligence using statistical and ML techniques.
• Model Lifecycle Management : Handle the full ML lifecycle from data preprocessing and model training to evaluation, deployment, monitoring, and continuous improvement.
• Model Optimization : Improve model performance with techniques like hyperparameter tuning, cross-validation, transfer learning, and quantization (e.g., QLoRA).
• Collaboration : Work closely with cybersecurity analysts, software engineers, and the R&D team to embed ML and LLM-based solutions into SanerNow’s workflows.
• Research and Innovation : Stay updated with the latest trends in AI, ML, LLMs, cybersecurity, and threat intelligence. Experiment with new tools and techniques to drive innovation.
• Documentation : Prepare detailed technical documentation and present findings and model behaviors to cross-functional teams.

Qualifications:

• Bachelor’s or Master’s degree in computer science, Data Science or a related field.
• Strong understanding of Transformers and core ML concepts and algorithms (e.g., boosting models, decision trees, SVM, KNN, clustering, dimensionality reduction).
• Proficiency in Python and ML libraries/frameworks like Scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers, etc.
• Practical experience in fine-tuning LLMs and working with embedding models (e.g., Sentence-BERT, OpenAI, Cohere).
• Experience building RAG architectures and working with vector databases.
• Knowledge of data preprocessing, EDA, model evaluation metrics, and deployment best practices.
• Familiarity with cybersecurity domain concepts, tools, and real-world threat detection workflows is a strong advantage.
• Experience with MLOps tools (e.g., MLflow, DVC, Docker, FastAPI) is a plus.
• Excellent analytical, problem-solving, and communication skills.