AI Engineer: Deploy & Optimize Real-World Models

Hired; Pakistan; On-site; PKR 1,800,000 - 3,200,000; Full time

Job summary

Hired is seeking an AI/ML Engineer to design, develop, and deploy machine learning models for real-world applications. You will work with cross-functional teams to integrate AI into production systems and improve models through iterative experimentation.

The role involves developing models with TensorFlow, PyTorch, or scikit-learn, preprocessing data, and ensuring scalable, low-latency inference in production environments.

Qualifications

• Proficiency in Python and ML libraries such as TensorFlow, PyTorch, or scikit-learn.
• Hands-on experience with data preprocessing, feature engineering, and model evaluation metrics.
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization tools like Docker and Kubernetes.
• Knowledge of MLOps practices, including model versioning, CI/CD pipelines, and monitoring frameworks.
• Experience with distributed computing frameworks (Spark, Dask) for handling large-scale datasets.

Responsibilities

• Develop and optimize machine learning models for real-world applications.
• Collaborate with data scientists and engineers to preprocess data, engineer features, and validate accuracy.
• Deploy AI models into scalable production environments with low-latency inference.
• Monitor model performance in live environments and implement retraining pipelines.
• Contribute to AI-driven product design, including recommender systems, computer vision, or NLP tools.

Skills

Python programming

ML model development

Data preprocessing

Model evaluation

Cloud platforms

CI/CD

MLOps

Distributed computing

Tools

TensorFlow

PyTorch

scikit-learn

Docker

Kubernetes

Job description

Hired is seeking an AI/ML Engineer to design, develop, and deploy machine learning models for real-world applications. You will work with cross-functional teams to integrate AI into production systems and improve models through iterative experimentation.

The role involves developing models with TensorFlow, PyTorch, or scikit-learn, preprocessing data, and ensuring scalable, low-latency inference in production environments.