Machine Learning Engineer (Intern / Junior)

Role Overview

Cyberfact Security is on a mission to integrate cutting-edge machine learning with advanced cybersecurity infrastructure. As a Machine Learning Engineer Intern or Junior Developer, you'll contribute to building intelligent systems that power our secure SaaS platforms, AI-enabled detection engines, and automation pipelines.

You'll get hands-on experience in model training, optimization, and real-time deployment, working closely with a cross-functional team of cybersecurity professionals, DevOps engineers, and full-stack developers.

This role offers an excellent opportunity for young professionals or final-year students looking to break into real-world machine learning with high-impact, secure applications.

Perks & Learning Benefits:

• Work on real-world cybersecurity + AI projects

• Get hands-on mentoring from senior engineers and researchers

• Gain exposure to MLOps and deployment tools

• Certification of completion + Letter of Recommendation (for interns)

• Opportunity for full-time offer based on performance

• GitHub project contributions under Cyberfact Security’s AI portfolio

Key Responsibilities

Key Responsibilities:

As a part of the AI & Data Science team, your tasks will include:

•  Model Development:
  Design, build, and train machine learning models using Python libraries like scikit-learn, TensorFlow, and Keras for structured and unstructured data.

•  Data Preprocessing & Feature Engineering:
  Work with real-world datasets, perform data cleaning, normalization, transformation, and create optimized feature pipelines using pandas and NumPy.

•  Model Evaluation & Tuning:
  Analyze model performance using metrics like accuracy, precision, recall, ROC-AUC, and apply techniques like hyperparameter tuning and cross-validation to optimize results.

•  Integration & Deployment:
  Package ML models using Flask/FastAPI and deploy via RESTful APIs, connecting them to frontend or backend systems within secure environments.

•  Automation & Versioning:
  Support ML workflows using MLOps practices, collaborate using Git, and implement model version control for experiments and rollbacks.

Required Qualifications

Qualifications:

We’re looking for passionate, self-driven individuals with the following background:

•  Educational Background:
  B.Tech / M.Tech / B.Sc / M.Sc in Computer Science, Artificial Intelligence, Data Science, or related fields.
  (Final year students with strong skills may also apply.)

•  Programming Skills:
  Proficiency in Python with good command over libraries like scikit-learn, pandas, matplotlib, seaborn, TensorFlow, and Keras.

•  Analytical Thinking:
  Ability to understand datasets, extract patterns, and frame ML problems effectively.

•  Version Control & Collaboration:
  Basic experience with Git, GitHub, and collaborative coding practices.

•  Mindset:
  Curiosity-driven, eager to learn, and passionate about building intelligent, secure systems.

Bonus Exposure (Preferred but not Mandatory):

If you’ve worked with or are curious about these tools/skills, it’s a plus:

• MLOps Platforms:
  MLflow, DVC, Kubeflow, or Weights & Biases for pipeline tracking and deployment.

• NLP & Transformers:
  Working with Hugging Face Transformers or fine-tuning pre-trained models for classification or generation tasks.

• Model Serving & APIs:
  Deployment with FastAPI, Flask, or containerization via Docker.

• CI/CD for ML Models:
  Basic understanding of CI/CD pipelines for retraining, testing, and auto-deployment of ML systems.