Machine Learning Engineer

Overview

Job Title: ML Engineer

Experience: 3-5 years

Location: Remote/Hybrid (Bangalore)

Joining Timeline: Immediate

Budget: 17-19LPA(Inclusive of GST)

Key Responsibilitie

sData Science & Modelin

• gUnderstand business problems and convert them into ML problem statement
• sPerform EDA, feature engineering, and feature selectio
• nBuild and evaluate models using
• :Regression, classification, clusterin
• gTime-series forecastin
• gAnomaly detection and recommendation system
• sApply model evaluation techniques (cross-validation, bias-variance tradeoff, metrics selection

)ML Engineering & Deploymen

• tProductionize ML models using Python-based pipeline
• sBuild reusable training and inference pipeline
• sImplement model versioning, experiment tracking, and retraining workflow
• sDeploy models using APIs or batch pipeline
• sMonitor model performance, data drift, and prediction stabilit

yData Engineering Collaboratio

• nWork with structured and semi-structured data from multiple source
• sCollaborate with data engineers to
• :Define data schema
• sBuild feature pipeline
• sEnsure data quality and reliabilit

yStakeholder Communicatio

• nPresent insights, model results, and trade-offs to non-technical stakeholder
• sDocument assumptions, methodologies, and limitations clearl
• ySupport business decision-making with interpretable output

s
Required Skil

lsCore Technical Skil

• lsProgramming: Python (NumPy, Pandas, Scikit-lear
• n)ML Libraries: XGBoost, LightGBM, TensorFlow / PyTorch (working knowledg
• e)SQL: Strong querying and data manipulation skil
• lsStatistics: Probability, hypothesis testing, distributio
• nsModeling: Supervised & unsupervised ML, time-series basi

csML Engineering Skil

• lsExperience with model deployment (REST APIs, batch job
• s)Familiarity with Docker and CI/CD for ML workflo
• wsExperience with ML lifecycle management (experiments, versioning, monitorin
• g)Understanding of data leakage, drift, and retraining strategi

esCloud & Tools (Any One Stack is Fin

• e)AWS / GCP / Azure (S3, BigQuery, SageMaker, Vertex AI, etc
• .)Workflow tools: Airflow, Prefect, or simil
• arExperiment tracking: MLflow, Weights & Biases (preferre

d)
Good to H

• aveExperience in domains like manufacturing, supply chain, fintech, retail, or consumer t
• echExposure to recommendation systems, forecasting, or optimizat
• ionKnowledge of feature stores and real-time inference syst
• emsExperience working with large-scale or noisy real-world datas

ets
Educational Qualifica

• tionBachelor’s or master’s degree in computer science, Statistics, Mathematics, Engineering, or related fi

elds