Machine Learning Engineer - Computer Vision & LLMs

Crowdception Inc; Lahore; On-site; PKR 1,800,000 - 3,600,000; Full time

Benefits offered by this job

Impactful work

Competitive pay

Onsite culture Lahore

Job summary

Crowdception Inc in Lahore is seeking an AI/ML engineer to design, train, and deploy advanced CV, LLM, and multimodal AI systems for healthcare applications. You will build low-latency inference pipelines and optimize deep learning architectures for secure, scalable production.

Collaborate with software engineers and domain experts to integrate AI models into end-user healthcare solutions while upholding strict data privacy standards and reliable performance.

Qualifications

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

Responsibilities

• Architect, train, and fine-tune CV and Multimodal AI models for healthcare.
• Deploy and optimize LLMs, RAG pipelines, and prompts for medical data.
• Design low-latency, scalable real-time inference infrastructure for production.
• Process and structure complex medical datasets while upholding data privacy.
• Benchmark and optimize models; monitor CI and deployment pipelines.
• Collaborate with software engineers, product teams, and domain experts to integrate AI into applications.

Skills

PyTorch

TensorFlow

Deep Learning

Computer Vision

LLMs

Multimodal AI

Python

REST/gRPC

Git

MLOps

Tools

Docker

Git

ONNX

FastAPI

Triton

Job description

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.