Mega Walkin Drive - AIML

HCLTech; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Job summary

HCLTech in Bengaluru is hosting a walk-in drive for Machine Learning Engineer, Data Scientist, or Data Analyst roles. We are seeking candidates to design, develop, and deploy ML models, perform data preprocessing, and extract actionable insights.

You will work with large datasets, perform EDA, feature engineering, model tuning, and deploy to production. Strong Python, ML libraries, and data visualization skills are required.

Qualifications

• Strong proficiency in Python for ML and data analysis.
• Hands-on experience with Scikit-learn, TensorFlow, Keras, PyTorch or similar ML libraries.
• Proficient in Pandas, NumPy, and Matplotlib for data manipulation and analysis.

Responsibilities

• Gather, clean, and preprocess structured, semi-structured, and unstructured data from various sources.
• Perform exploratory data analysis to identify trends, patterns and outliers.
• Build, train, and fine-tune ML models; deploy to production environments.
• Hyperparameter optimization using grid/random search; evaluate using accuracy, precision, recall, F1, AUC-ROC.
• Develop dashboards/reports with Tableau, Power BI, Matplotlib/Seaborn/Plotly; present findings to stakeholders.

Skills

Python programming

ML libraries

Pandas & NumPy

Statistical analysis

Cloud platforms

Data visualization

SQL & NoSQL

Data cleaning & preprocessing

Tools

Airflow

Apache Kafka

Celery

BigQuery

Job description

Please mark your attendance for the Walkin Drive below

QRCode for Attendance Confirmation- Mega Drive - 22nd Aug'26- Data&AI (1) 2.png

Job Overview:

We are seeking a skilled Machine Learning Engineer, Data Scientist, or Data Analyst to design, develop, and deploy machine learning models, conduct deep data analysis, and generate actionable insights. The ideal candidate will have experience in data preprocessing, feature engineering, model development, and performance optimization, working with large datasets and leveraging advanced machine learning frameworks.

Key Responsibilities

• :Data Preparation & Analysis
• :Gather, clean, and preprocess structured, semi-structured, and unstructured data from various sources
• .Conduct exploratory data analysis (EDA) to identify trends, patterns, and outliers
• .Apply data wrangling techniques using Pandas, NumPy, and SQL to transform raw data into usable formats
• .Use statistical analysis to drive data-driven decision-making
• :Build, train, and fine-tune machine learning models using Scikit-learn, TensorFlow, Keras, or PyTorch
• .Develop predictive models, classification algorithms, clustering models, and recommendation systems
• .Conduct hyperparameter optimization using techniques like grid search or random search
• :Evaluate model performance using metrics such as Accuracy, Precision, Recall, F1-Score, AUC-ROC, Confusion Matrix, and Cross-validation
• .Improve model performance through techniques such as feature engineering, data augmentation, and regularization
• .Deploy models into production environments, and monitor performance for continual improvement
• :Develop dashboards and reports using Tableau, Power BI, Matplotlib, Seaborn, or Plotly
• .Present findings through clear visualizations and actionable insights to non-technical stakeholders
• .Write detailed reports on data analysis and machine learning results, ensuring transparency and reproducibility
• .Collaboration & Stakeholder Communication
• :Work closely with cross-functional teams (e.g., engineering, product, business) to define data-driven solutions
• .Communicate technical concepts clearly to non-technical stakeholders and provide insights that influence product and business strategy
• :Design and implement scalable data pipelines for model training and deployment using Airflow, Apache Kafka, or Celery
• .Automate data collection, preprocessing, and feature extraction tasks
• .Research & Continuous Learning
• :Stay up-to-date with the latest trends in machine learning, deep learning, and data science methodologies
• .Explore new tools, techniques, and frameworks to improve model accuracy and efficiency

.

Required Skill

• s:Programming Languages: Strong proficiency in Python, with experience in SQ
• L.Machine Learning: Hands-on experience with Scikit-learn, TensorFlow, Keras, PyTorch, or similar ML librarie
• s.Data Analysis: Strong skills in Pandas, NumPy, and Matplotlib for data manipulation and analysi
• s.Statistical Analysis: Experience applying statistical methods to data, including hypothesis testing and regression analysi
• s.Cloud Platforms: Familiarity with AWS, Azure, or Google Cloud for deploying models and using cloud-native data services (e.g., AWS Sagemaker, Azure ML
• ).Data Visualization: Experience using Tableau, Power BI, Matplotlib, Seaborn, or Plotly for creating visualization
• s.SQL & Databases: Proficiency in SQL for querying relational databases and working with NoSQL databases (e.g., MongoDB, BigQuery
• ).Version Control: Experience using Git for version contro

l.

• ls:Big Data Technologies: Familiarity with tools like Apache Hadoop, Spark, Dask, or Google BigQuery for processing large datase
• ts.Deep Learning: Experience with deep learning frameworks such as TensorFlow, PyTorch, or MXN
• et.NLP & Computer Vision: Experience with natural language processing (NLP) using spaCy, NLTK, or transformers, and computer vision using OpenCV or TensorFl
• ow.MLOps: Familiarity with MLOps tools like Kubeflow, MLflow, or DVC for managing model workflo
• ws.Data Engineering: Experience with ETL tools like Apache Airflow, Talend, AWS Glue, or Google Dataflow for data pipeline automati

on.

Tools & Technolog

• ies:Machine Learning: Scikit-learn, TensorFlow, PyTorch, Keras, XGBo
• tly.Cloud Platforms: AWS, Google Cloud, Az