Machine Learning Engineer

SoftMaji; Lucknow; On-site; INR 700,000 - 1,600,000; Full time

Benefits offered by this job

Competitive salary

Certifications support

Dev tools access

Real business applications

AI/cloud exposure

Production ML systems

Career growth

Senior ML path

Job summary

SoftMaji is seeking a Machine Learning Engineer in Lucknow to develop, test and deploy ML systems for web applications and SaaS products. You will handle data preparation, model development and integration with APIs and production environments.

The role involves building models for prediction and classification, evaluating performance, and collaborating with backend engineers. 2–5 years of hands-on ML experience is preferred, with a strong Python and ML toolkit background.

Qualifications

• 2 to 5 years of practical ML experience
• Strong Python and ML project exposure
• Comfort with building and deploying models
• Good knowledge of datasets and preprocessing

Responsibilities

• Develop and deploy machine learning models
• Prepare, clean and analyse datasets
• Build classification and prediction systems
• Work with text and document data
• Train, test and evaluate models
• Improve model performance and reliability
• Create APIs for ML services
• Integrate models with web and mobile apps
• Monitor model performance post-deployment
• Coordinate with backend and data engineering teams
• Document models, datasets and processes
• Troubleshoot production ML issues

Skills

Python

NumPy/Pandas

Scikit-learn

ML algorithms

Data cleaning

Model evaluation

SQL

REST APIs

Git/GitHub

Linux basics

Statistics

Tools

PyTorch

TensorFlow

NLP

Computer Vision

OCR

Time-series

FastAPI/Flask

Docker

ML deployment

Cloud platforms

Job description

Location: Lucknow, Uttar Pradesh
Job Type: Full Time
Experience: 2 to 5 Years
Salary: ₹7 LPA to ₹16 LPA

Job Description

We are looking for a Machine Learning Engineer to develop, test and deploy machine learning systems for our web applications, SaaS products and business solutions.

You will work on practical problems involving prediction, classification, recommendation, document processing and data analysis. The role includes working with data, developing models and making sure those models work properly inside real applications.

Key Responsibilities

• Develop and deploy machine learning models
• Prepare, clean and analyse datasets
• Build classification and prediction systems
• Work with text and document data
• Train, test and evaluate models
• Improve model performance and reliability
• Create APIs for machine learning services
• Integrate models with web and mobile applications
• Monitor model performance after deployment
• Work with backend and data engineering teams
• Document models, datasets and development processes
• Troubleshoot issues in production ML systems

Required Skills

• Strong Python knowledge
• NumPy and Pandas
• Scikit-learn
• Basic understanding of machine learning algorithms
• Data cleaning and preprocessing
• Model evaluation
• SQL
• REST APIs
• Git and GitHub
• Basic Linux knowledge
• Good understanding of statistics and programming fundamentals

Good to Have

• PyTorch or TensorFlow
• NLP
• Computer Vision
• OCR
• Recommendation systems
• Time-series analysis
• FastAPI or Flask
• Docker
• ML model deployment
• Cloud platforms
• Experience working with large datasets

Who Can Apply

Candidates with 2 to 5 years of practical Machine Learning experience can apply.

Candidates with strong Python, mathematics, machine learning projects and good understanding of model development are also encouraged to apply.

Perks & Benefits

• Competitive salary with performance-based growth
• Learning and certification support
• Access to professional development tools
• Work on real business applications
• Exposure to AI, cloud, data and software engineering
• Opportunity to build and deploy production ML systems
• Technical learning and career growth
• Opportunity to progress toward Senior ML Engineer / ML Lead

What We Are Looking For

Someone who can take a machine learning problem from data preparation and model development through testing, API integration and production deployment.