Machine Learning Engineer

Emperen Technologies; New Delhi; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Emperen Technologies in Delhi, India seeks a skilled Machine Learning Engineer with 5+ years of experience to design, implement, and deploy scalable ML models. You will collaborate with data scientists, software engineers, and product teams to integrate ML into production systems and solve complex mathematical problems.

The role requires a deep understanding of linear algebra, statistics, calculus, and optimization, and familiarity with ML frameworks and MLOps tools.

Qualifications

• 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.

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.

Skills

Python

NumPy

Pandas

Scikit-learn

TensorFlow

PyTorch

Keras

ML Ops

Docker

Kubernetes

AWS

GCP

Azure

Spark

Dask

Hyperparameter tuning

Feature engineering

Education

Master's or Ph.D. in CS/Math/Stats

Tools

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Spark

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