Job Title: Jr. ML Engineer

Experience Range:

Industry: 3–5 years Academic: 1–2 years (relevant ML/AI research or projects)

Must Have Skills

Programming: Strong expertise in Python or R Applied Machine Learning: Problem framing – ability to choose between Supervised, Self-supervised, or Reinforcement Learning (RL) Data wrangling – experience with Weak/Distant Supervision, Pseudo-labelling, strong EDA, data preparation, labelling, and data augmentation End-to-end modelling in ML, DL, and RL Experience with Single models, Ensembles, Mixture of Experts Understanding of Mathematical Induction, Tree Induction, Deep Learning fundamentals, and optimization algorithms like SGD Transfer learning – N-shot learning (or variants), fine-tuning skills ML/DL Verticals: Expertise in at least one – Time Series Modelling, Computer Vision, NLP, or RL Portfolio: GitHub with original ML repositories Kaggle with decent leaderboard positions Tools & Libraries: Proficiency in TensorFlow or PyTorch MLOps: Experience running ML experiments Deploying ML solutions into production (TFServe, Seldon, custom serving – interactive, batch, and stream serving) Performance and scalability optimization Data Engineering: Ensuring smooth data flow between databases and backend systems Custom Development: Implementing custom ML algorithms (e.g., custom SGD) and designing own DNN architectures when needed

Good to Have Skills

Experience with Kubeflow, MLflow, Airflow, or SparkML AI/Automation-related patents Exposure to statistics and probability Dockerized code execution experience Familiarity with Kubernetes (preferred as the company uses it) Computer Science or IT background