Machine Learning Engineer

Emperen Technologies; Kanpur; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Emperen Technologies in Kanpur is looking for a highly skilled Machine Learning Engineer to join our AI team. The ideal candidate should possess 5+ years of experience and a deep understanding of algorithms, statistical modeling, and optimization techniques. Responsibilities include designing and deploying ML models, collaborating with teams for production deployment, and staying updated with the latest research.

A strong foundation in mathematics and proficiency in programming languages like Python is essential.

Qualifications

• 5+ years of experience in machine learning.
• Strong foundation in Linear Algebra.
• Proficiency in Probability and Statistics.

Responsibilities

• Design, develop, and deploy machine learning models.
• Collaborate with teams to integrate ML solutions.
• Optimize model performance through tuning and improvements.

Skills

Deep understanding of machine learning algorithms

Statistical modeling

Optimization techniques

Proficient in Python

Experience with deep learning frameworks

Education

Master's or Ph.D. in Computer Science, Mathematics, or related field

Tools

TensorFlow

PyTorch

AWS

Docker

Job description

Job Title

Machine Learning Engineer

Job Type

Full-Time

Experience Level

Mid to Senior (5+ Years)

Department

Data Science / AI Engineering

Job Summary

We are seeking a highly skilled and mathematically grounded Machine Learning Engineer to join our AI team.

The ideal candidate will have 5+ years of ML experience with a deep understanding of machine learning algorithms, statistical modeling, and optimization techniques, along with hands‑on experience in building scalable ML systems using modern frameworks and tools.

Key Responsibilities

• Design, develop, and deploy machine learning models for real‑world applications.
• Collaborate with data scientists, software engineers, and product teams to integrate ML solutions into production systems.
• Understand the mathematics behind machine learning algorithms to effectively implement and optimize them.
• Conduct mathematical analysis of algorithms to ensure robustness, efficiency, and scalability.
• Optimize model performance through hyperparameter tuning, feature engineering, and algorithmic improvements.
• Stay updated with the latest research in machine learning and apply relevant findings to ongoing projects.

Required Qualifications

Mathematics & Theoretical Foundations

• Strong foundation in Linear Algebra (e.g., matrix operations, eigenvalues, SVD).
• Proficiency in Probability and Statistics (e.g., Bayesian inference, hypothesis testing, distributions).
• Solid understanding of Calculus (e.g., gradients, partial derivatives, optimization).
• Knowledge of Numerical Methods and Convex Optimization.
• Familiarity with Information Theory, Graph Theory, or Statistical Learning Theory is a plus.

Programming & Software Skills

• Proficient in Python (preferred), with experience in libraries such as NumPy, Pandas, Scikit‑learn, Matplotlib, Seaborn.
• Experience with deep learning frameworks: TensorFlow, PyTorch, Keras, or JAX.
• Familiarity with ML Ops tools: MLflow, Kubeflow, Airflow, Docker, Kubernetes.
• Experience with cloud platforms (AWS, GCP, Azure) for model deployment.

Machine Learning Expertise

• Hands‑on experience with supervised, unsupervised, and reinforcement learning.
• Understanding of model evaluation metrics and validation techniques.
• Experience with large‑scale data processing (e.g., Spark, Dask) is a plus.

Preferred Qualifications

• Master's or Ph.D. in Computer Science, Mathematics, Statistics, or a related field.
• Publications or contributions to open‑source ML projects.
• Experience with LLMs, transformers, or generative models.