Machine Learning Engineer - Computer Vision & LLMs

In this position, you will design, train, and deploy advanced Computer Vision (CV) models, Large Language Models (LLMs), and Multimodal AI systems. You will build low-latency inference pipelines, optimize deep learning architectures, and collaborate cross-functionally to deliver secure, scalable, and clinically relevant AI technologies.

Key Responsibilities

• Multimodal & CV Model Development: Architect, train, and fine-tune state-of-the-art Computer Vision algorithms and Multimodal AI models tailored for healthcare applications.
• LLM Integration & Fine-Tuning: Deploy and optimize Large Language Models (LLMs), retrieval-augmented generation (RAG) pipelines, and prompt engineering frameworks for domain-specific medical data.
• Real-Time AI Infrastructure: Design and operate low-latency, scalable serving infrastructure for real-time inference in production healthcare environments.
• Data Engineering & Preprocessing: Process, clean, and structure complex medical datasets (image, text, and multimodal streams) while maintaining strict data privacy standards.
• Model Optimization & MLOps: Benchmark, quantize, and optimize deep learning models for performance, monitoring continuous integration and automated deployment pipelines.
• Cross-Functional Collaboration: Partner with software engineers, product leads, and domain experts to integrate AI models into end-user healthcare applications.

Requirements

AI/ML Technical Depth

• Deep Learning Frameworks: Hands-on experience developing in PyTorch or TensorFlow for production-level ML architectures.
• Computer Vision & LLMs: Demonstrated experience with modern CV architectures (segmentation, object detection, spatial models) and Transformer-based LLM architectures.
• Multimodal & Real-Time Systems: Practical knowledge of fusing multimodal data streams and deploying low-latency, real-time AI pipelines.
• Software Engineering Standards: Strong proficiency in Python, object-oriented programming, REST/gRPC API integration, and version control (Git).
• MLOps & Deployment: Familiarity with containerization (Docker), cloud infrastructure (AWS/GCP/Azure), and model serving frameworks (e.g., Triton, FastAPI, ONNX).
• Domain Focus: Passion for building high-reliability, compliant technology solutions within healthcare.

Total Rewards & Benefits

• Impactful Work: Direct involvement in building life-enhancing AI technologies for the global healthcare ecosystem.
• Market-Competitive Compensation: Competitive base salary reflecting technical expertise and experience.
• Onsite Engineering Culture: Collaborative, fast-paced environment located in Lahore with access to high-performance computing resources and modern AI tooling.