Website IBM

IBM Data Engineer – Machine Learning (Gurgaon, India)

Opportunity Overview

Attribute Details
Organisation IBM Consulting (Client Innovation Center / Delivery Center)
Role Title Data Engineer – Machine Learning
Job Location Gurgaon, Haryana, India
Domain Focus Machine Learning Pipelines, Feature Engineering, Model Optimisation, Enterprise Consulting
Experience Level Senior / Lead Professional (5 to 15 Years)
Education Requirement Bachelor’s Degree in Computer Science, Data Science, or related field (Master’s preferred)
Application Fee Free

Role Scope & Technical Architecture

The Data Engineer – Machine Learning bridges big data engineering pipelines and statistical machine learning models to deploy scalable, high-impact enterprise AI solutions across IBM Consulting clients:

[Enterprise Data Extraction & Cleansing] ➔ [Feature Selection & Engineering] ➔ [Model Selection & Hyperparameter Tuning] ➔ [Deployment, Monitoring & Stakeholder Reporting]

Core Responsibilities

  • ML Solution Development: Apply machine learning and statistical concepts to solve complex business problems, interpret multi-dimensional data, and design optimal feature engineering workflows.

  • Algorithm Selection & Evaluation: Select appropriate classical ML and modern deep learning algorithms; continuously evaluate performance using domain-specific metrics (Precision/Recall, F1-Score, ROC-AUC, RMSE).

  • Advanced Pipeline Optimisation: Fine-tune complex algorithms for optimal runtime efficiency, enterprise scale, and low-latency inference.

  • Technical Communication & Stakeholder Alignment: Translate complex statistical outputs and model metrics into actionable business insights for non-technical client executives and technical teams.

Candidate Eligibility & Technical Skills Matrix

Minimum Qualifications

  • Professional Experience: 5 to 15 years of progressive experience in Data Engineering, Machine Learning Engineering, or Applied Data Science.

  • Education: Bachelor’s degree in Computer Science, Quantitative Studies, or related discipline.

  • Core Competencies: Proven expertise in statistical analysis, feature extraction, model selection, algorithm evaluation, and technical stakeholder management.

Technical Skills Matrix

Category Skill Domain & Focus Areas
Machine Learning & Statistics

• Algorithms: Supervised/Unsupervised Learning, Regression, Ensemble methods (XGBoost, Random Forest), Clustering


• Metrics & Evaluation: Cross-validation, ROC-AUC, F1-Score, Bias-Variance tradeoff, Feature Importance
Data Engineering & Feature Pipelines

• Feature extraction, dimensionality reduction (PCA), data cleansing, and handling missing data at scale


• Data integration from heterogeneous relational, NoSQL, and cloud data warehouses
Specialized Domain Tools (Preferred)

• Advanced Domain: Natural Language Processing (NLP), Computer Vision (CV), or Time-Series Forecasting


• Visualization: Advanced dashboarding and model interpretability frameworks (SHAP, LIME)
Consulting & Leadership

• Translating client business requirements into statistical problem formulations


• Client-facing presentation, technical writing, and Agile delivery collaboration

Selection Workflow & Evaluation Stages

[Resume & Seniority Screening] ➔ [Technical Screening Call] ➔ [System Design & ML Modeling Loop] ➔ [Consulting Case Study & Leadership Round]

  1. Profile Screening: Verification of relevant 5–15 year track record, hands-on ML implementation background, and alignment with IBM Client Innovation Center projects.

  2. Technical Screening: Assessment of statistical fundamentals, core computer science concepts, Python ML ecosystem proficiency, and feature engineering logic.

  3. Deep Dive Technical Loop:

    • Machine Learning & System Design: Scenario-based architecture evaluation focusing on end-to-end ML pipeline scalability, algorithm selection, and metric trade-offs.

    • Data Pipeline & Optimisation Coding: Practical exercise on feature selection, debugging model drift, and optimizing algorithm performance under high-volume data constraints.

  4. Consulting & Partner Loop: Client-facing case study presentation testing structured problem-solving, stakeholder management, business value articulation, and consulting mindset.

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