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Role Roadmap

ML / GenAI Engineer

Productionise ML and LLM systems — training pipelines, RAG, evaluation, and serving.

6 technologies in this stack — ordered Core → Edge → Hybrid bonus. Click any tile to start practicing.

Core

Core — lands the role. Baseline skills hiring managers screen for. Master these and you can land the role at market CTC.

Edge

Edge — lifts your CTC band. Rarer skills that strengthen negotiation — they can push the offer toward the top of the band (e.g. ₹30–40L+ for senior data roles).

Hybrid bonus

Hybrid bonus — broadens your profile. Cross-domain extras (e.g. Power BI for a Data Engineer) that open hybrid roles and raise your profile weightage with recruiters.

  1. Core
    1

    Machine Learning

    Algorithms, evaluation metrics, bias/variance, feature engineering, and model selection.

    Open practice tracks →
  2. Core
    2

    MLflow & Experiment Tracking

    Experiment tracking, model registry, serving, and MLOps workflows.

    Open practice tracks →
  3. Core
    3

    Python for Data Engineering

    Python scripting, data wrangling, OOP, and performance optimization for data pipelines.

    Open practice tracks →
  4. Edge
    4

    LLM Engineering

    RAG pipelines, prompt engineering, fine-tuning, vector databases, and LLM evaluation.

    Open practice tracks →
  5. Edge
    5

    Vector Databases

    Pinecone, Weaviate, pgvector, Chroma — embeddings, similarity search, and RAG.

    Open practice tracks →
  6. Hybrid bonus
    6

    Pandas & NumPy

    DataFrames, vectorized operations, performance optimization, and common patterns.

    Open practice tracks →