Machine Learning Engineer

HCLTech; Dadri; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

A leading technology company based in India is seeking a Machine Learning Engineer to design, build, and deploy machine learning models powering intelligent agent systems. The ideal candidate will have hands-on experience with deploying ML models at scale, optimizing them for performance, and collaborating with cross-functional teams. Strong proficiency in Python, TensorFlow, and PyTorch is essential, along with familiarity with MLOps and cloud platforms like AWS or GCP. Join us to contribute to innovative AI solutions.

Qualifications

• Proficiency in Python, TensorFlow, and PyTorch.
• Experience with ML pipelines and MLOps tools (MLflow, Kubeflow).
• Strong understanding of data preprocessing and feature engineering.
• Familiarity with cloud platforms (AWS, Azure, GCP) for ML deployment.

Responsibilities

• Hands-on experience in deploying ML models at scale.
• Design and implement machine learning models for various tasks.
• Build and maintain scalable ML pipelines for data.
• Collaborate with team to integrate models into workflows.
• Monitor and improve model performance post-deployment.
• Ensure compliance with best practices for security and AI.

Skills

Python

TensorFlow

PyTorch

ML pipelines

MLflow

Kubeflow

data preprocessing

feature engineering

model evaluation metrics

cloud platforms

Docker

Kubernetes

CI/CD pipelines

Job description

We are looking for a Machine Learning Engineer to design, build, and deploy ML models that power intelligent agent systems and enterprise AI solutions.

The Machine Learning Engineer will be responsible for developing scalable ML pipelines, optimizing models for performance, and ensuring robust deployment in production environments. This role requires strong technical expertise in ML algorithms and practical experience in MLOps.

Responsibilities

• Hands‑on experience in deploying ML models at scale with automated monitoring and retraining in production environments.
• Design and implement machine learning models for prediction, classification, and optimization tasks.
• Build and maintain scalable ML pipelines for data ingestion, training, and deployment.
• Collaborate with data scientists and engineers to integrate models into agent workflows.
• Monitor and improve model performance post‑deployment using automated retraining strategies.
• Ensure compliance with best practices for security, reliability, and ethical AI.

Mandatory Skills

• Proficiency in Python, TensorFlow, and PyTorch.
• Experience with ML pipelines and MLOps tools (MLflow, Kubeflow).
• Strong understanding of data preprocessing, feature engineering, and model evaluation metrics.
• Familiarity with cloud platforms (AWS, Azure, GCP) for ML deployment.

Additional Skills

• Knowledge of LLM integration and hybrid AI systems.
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Exposure to CI/CD pipelines for ML workflows.