AI/ML Engineer

Infotech is seeking a talented AI/ML Engineer with expertise in machine learning algorithms, deep learning, and prompt engineering to join their team. This role focuses on designing, developing, and deploying scalable AI-driven solutions by leveraging large, real-world datasets and advanced model development techniques. The ideal candidate has hands-on experience in building, optimizing, and integrating machine learning models into production environments while ensuring efficient performance and resource utilization. The position requires collaboration with engineering and operations teams and is based onsite in Karachi. This role involves working with diverse AI technologies such as convolutional neural networks, recurrent neural networks, and transformer architectures, as well as designing and optimizing prompts for large language models (LLMs). The engineer will implement end-to-end solutions covering data preprocessing, model evaluation, deployment using modern MLOps practices, containerization, and cloud technologies. Although this role does not include team management responsibilities, the candidate must demonstrate strong problem-solving skills and a collaborative mindset in a dynamic and fast-paced environment.
Responsibilities
Design, develop, and deploy machine learning models tailored for production use cases, ensuring robustness and scalability.

Perform data preprocessing and feature engineering on large, noisy real-world datasets to create high-quality inputs for modeling.

Implement a variety of supervised and unsupervised learning algorithms, including classification, regression, clustering, and ensemble techniques.

Build and optimize deep learning models using neural network architectures such as CNNs, RNNs, and Transformers to address various AI tasks.

Design, test, and refine prompts for Large Language Models to maximize model effectiveness, accuracy, and user experience.

Continuously evaluate model performance using relevant metrics and apply improvements to enhance accuracy and efficiency.

Optimize machine learning models to meet latency and throughput requirements while managing resource utilization effectively.

Develop and integrate AI/ML solutions with APIs and microservices to enable seamless interaction within software ecosystems.

Collaborate closely with engineering and DevOps teams to deploy, scale, monitor, log, and maintain AI systems ensuring operational stability.

Implement monitoring, logging, backup, and disaster recovery protocols for deployed machine learning applications.

Utilize containerization and orchestration technologies such as Docker and Kubernetes to streamline deployment processes.

Employ MLOps tools and workflows including MLflow, Kubeflow, and Airflow to maintain reliable model lifecycle management.

Stay up to date with cloud platform services (AWS, Azure, GCP) and integrate AI/ML solutions effectively within these environments.

Work in an agile development environment, contributing to iterative development and continuous improvement.

Apply strong analytical and problem-solving skills to address challenges encountered in model deployment and production maintenance.