Machine Learning Engineer

HCLTech; Bengaluru; On-site; INR 1,200,000 - 2,200,000; Full time

Job summary

HCLTech is seeking a versatile Machine Learning Engineer/Data Scientist/Data Analyst to design, develop, and deploy ML models and conduct deep data analysis. You will preprocess data, engineer features, build models, and optimize performance on large datasets.

Responsibilities include data gathering, EDA, model training, evaluation with metrics, and delivering actionable insights. You'll collaborate with cross‑functional teams to deploy scalable data pipelines and create visual reports for

Qualifications

• Python proficiency with strong software development skills.

Responsibilities

• Data preparation, cleaning, and preprocessing from diverse sources.

Skills

Python

Scikit-learn

TensorFlow

Keras

PyTorch

Pandas

NumPy

Matplotlib

Seaborn

Plotly

SQL

NoSQL

Git

Hadoop

Spark

Airflow

Kubeflow

MLflow

ETL

OpenCV

SpaCy

NLTK

Tools

XGBoost

Tableau

Power BI

Matplotlib

Seaborn

Plotly

Apache Kafka

Airflow

MongoDB

BigQuery

TensorFlow

PyTorch

Job description

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 usingPandas,NumPy, andSQL to transform raw data into usable formats
• .Use statistical analysis to drive data-driven decision-making
• :Build, train, and fine-tune machine learning models usingScikit-learn,TensorFlow,Keras, orPyTorch
• .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 usingTableau,Power BI,Matplotlib,Seaborn, orPlotly
• .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 usingAirflow,Apache Kafka, orCelery
• .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
• 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

Tools & Technolog

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