Job Description

Skills
Skill


Minimum Qualification


No data available

Key Responsibilities

End-to-End Model Training:

Design, train, and fine-tune Machine Learning and Deep Learning models from scratch. You know how to select the right architecture (CNN, RNN, Transformer) for the problem.

Inference & Deployment:

You don't just stop at training. You are responsible for running models in production, optimizing them for latency, and exposing them via APIs (FastAPI/Flask).

Deep Understanding:

You can debug a model not just by changing code, but by analyzing loss curves, adjusting learning rates, and fixing data imbalances. You know whythe model is failing.

Code Quality:

Write clean, modular, and production-ready Python code. Your code is testable, version-controlled, and scalable.

Learn & Adapt:

Collaborate with seniors to learn

Knowledge Graph

technologies (Neo4j, RDF) and your AI skills to graph-based problems (e.g., Graph Neural Networks).

Must-Have Skills

Fundamental knowledge or AWS or a similar cloud platform

AI & Math Fundamentals:

Strong grasp of the theory behind ML--you understand gradient descent, backpropagation, activation functions, and overfitting/underfitting concepts.

Deep Learning Frameworks:

3+ years of experience with

PyTorch

or

TensorFlow

. You can write custom training loops and data loaders.

Python Mastery:

Expert-level Python skills. You understand object-oriented programming, decorators, and memory management.

Data Engineering for AI:

Ability to build efficient data pipelines (Pandas/NumPy) to preprocess complex datasets before feeding them into models.

Model Evaluation:

Experience setting up robust validation strategies (Cross-validation, F1-score, AUC-ROC) to ensure models actually work on unseen data.

Nice to Have

Experience with LLMs (Large Language Models) or NLP. Exposure to graph databases (Neo4j, Neptune) or network analysis. * Experience deploying models using Docker or Kubernetes.