Senior Machine Learning Engineer

GreyOrange; Gurugram District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

GreyOrange is seeking a highly skilled Machine Learning Engineer (SDE-3) capable of owning end-to-end ML system development, including model building, production deployment, and MLOps pipeline management.

The role involves building scalable ML solutions for real-world guided automation with a strong coding background in Python and/or Java, plus hands-on experience with TensorFlow and PyTorch. You will work across product, data engineering, and platform teams to deliver impactful AI applications.

Qualifications

• Proficient in Python and/or Java; write clean, production-ready code.
• Strong ML knowledge: regression, classification, deep learning, LLMs.
• Hands-on experience with ML frameworks such as TensorFlow and PyTorch.
• Experience with ML deployment, CI/CD for ML, and MLOps.
• Ability to translate business problems into scalable ML solutions.

Responsibilities

• Own end-to-end ML lifecycle: problem formulation, model development, evaluation, deployment and monitoring.
• Design and build scalable ML systems for real-world applications.
• Develop and maintain MLOps pipelines, data ingestion, training, retraining, CI/CD, monitoring.
• Work on ML problems including regression, classification, deep learning, and LLM-based apps.
• Collaborate with Product, Data Engineering, and Platform teams to deliver ML solutions.

Skills

Python

Java

Analytical thinking

Production-grade code

Tools

TensorFlow

PyTorch

Job description

We are looking for a highly skilled Machine Learning Engineer (SDE-3) who can own end‑to‑end ML system development, including model building, production deployment, and MLOps pipeline management. The ideal candidate should have strong coding ability, excellent analytical thinking, and hands‑on experience building scalable ML systems in production environments. This role offers an opportunity to work on cutting‑edge AI solutions, including Deep Learning and LLM‑based applications, powering intelligent automation systems.

End‑to‑End ML System Development

• Own the complete ML lifecycle: Problem formulation, model development, evaluation and optimisation, production deployment, and monitoring.
• Design and build scalable ML systems for real‑world applications.

MLOps And Production Engineering

• Develop and maintain MLOps pipelines, including data ingestion and preprocessing, model training and retraining workflows, CI/CD pipelines for ML models, and monitoring model performance and drift.
• Ensure production‑grade reliability through model versioning, performance optimisation, fault tolerance, and observability.

Machine Learning And AI Applications

• Work on a variety of ML problems, including regression and classification, deep learning models, LLM‑based applications, and integrations.
• Build scalable inference and deployment systems for AI‑driven applications.

Cross‑Functional Collaboration

• Collaborate with Product, Data Engineering, and Platform teams to deliver impactful ML solutions.
• Translate business problems into scalable machine learning solutions.

Core Requirements

Programming and Software Engineering

• Strong programming skills in Python and/or Java.
• Ability to write clean, maintainable, and production‑quality code.

Machine Learning Expertise

• Strong understanding of regression models, classification algorithms, deep learning techniques, large language models (LLMs), and practical applications.
• Hands‑on experience with ML frameworks such as TensorFlow and PyTorch.

MLOps And Deployment

• Experience with model deployment (batch and real‑time), CI/CD pipelines for ML, monitoring and logging systems, and data pipeline integration.
• Experience building and managing scalable ML infrastructure in production.

Analytical Thinking

• Strong problem‑solving and analytical skills.
• Ability to reason about trade‑offs, scalability, and model performance.

Good To Have

• Strong ownership mindset, taking solutions from idea to production.
• Ability to work independently and drive technical decisions.
• Curious to stay updated with the latest trends in ML and AI.
• Ability to balance theoretical concepts with practical system constraints.