Machine Learning Engineer

Machine Learning Engineer (A2)

Experience:

2–4 Years

Location:

Gurugram

Role Summary

We are looking for a Machine Learning Engineer with 2–4 years of experience to help

scale our search and recommendation infrastructure. This role focuses on the end-to

end lifecycle of ML products: from building large-scale data pipelines to deploying high

availability models in production.

You will be responsible for building robust PySpark ETLs, developing PyTorch-based

models, and managing Vector Databases to power real-time discovery. While the core

applications are traditional search and recommendations, you will also be responsible

for fine-tuning LLMs/SLMs for specific use cases.

Key Responsibilities

• Architect and maintain scalable ETL pipelines using PySpark to process large

datasets for feature engineering and model training.

• Build and optimize production-grade models using PyTorch.

• Implement and optimize Vector Databases for high-dimensional similarity

search and retrieval.

• Fine-tune LLMs/SLMs for specific search and recommendation tasks, such as

semantic query understanding.

• Deploy models into production environments as real-time services using

inference frameworks like Triton Inference Server, Bento

ML, or Tensor Flow

Serving.

• Deploy models into production environments as real-time services, ensuring

adherence to strict SLAs regarding latency and throughput.

• Implement robust monitoring and logging to track model performance, data

drift, and system health in a live environment.

Technical Requirements

• Expert-level proficiency in Python and SQL.

• Proven experience with PySpark and distributed computing.

• Strong hands-on experience building and optimizing production-grade models

using PyTorch.

• Practical knowledge of Vector Databases and embedding-based retrieval

techniques.

• Experience fine-tuning open-source LLMs/SLMs for specialized downstream

tasks.

• Proficiency with core scientific libraries including Num Py, Sci Py, and Matplotlib,

Pandas, Scikit-learn, XGBoost/Light

GBM, and Hugging Face Transformer

• Familiarity with experiment tracking and model versioning tools like MLflow.

• Experience with Docker, Kubernetes, and building high-performance APIs.

• Professional Qualifications

• 2–4 years of experience as an ML Engineer or Data Scientist in a production

focused environment.

• Deep understanding of the trade-offs between model complexity and real-time

inference latency.

• Ability to own a project from the data-collection phase through to production

deployment and maintenance.