AI Engineer

Role Overview

Reporting to AI Lead / Solution Architect, this is a senior technical leadership role responsible for:

· Designing and deploying state-of-the-art LLMs and multi-modal AI models.

· Implementing agentic AI systems with RAG, self-RAG, and memory-optimized architectures.

· Leading cloud-based GPU LLM training pipelines, including data labeling, preprocessing, model training, and post-processing.

· Extending AI models into front-end applications and back-end pipelines.

· Managing staging, development, and production environments with SSH deployment and editing.

Driving complex problem-solving, critical thinking, and innovative AI solutions

Key Responsibilities

1. LLM Cloud Training & Pipelines (Mandatory)

· Design and deploy LLM training pipelines on cloud GPU infrastructure.

· Manage data labeling, preprocessing, and post-processing for training datasets.

· Optimize model training, memory usage, and inference efficiency.

· Implement RAG-based pipelines, agentic RAG, and self-RAG for knowledge augmentation.

2. Deep Learning & Model Development

· Research, design, and implement Transformers, Vision Transformers, Diffusion Models.

· Optimize SAM models for tabular and image data, including preprocessing and post-processing.

· Implement multi-modal AI pipelines (text, image, video).

· Develop agentic AI models with adaptive memory and reasoning.

3. Agentic AI & Orchestration

· Design agent frameworks with orchestration, inter-agent communication, and protocol management.

· Integrate LangChain, LangGraph, or custom agent pipelines.

· Ensure AI agents are robust, scalable, and production-ready.

4. Machine Learning for Real-World Data

· Implement ML models for tabular, image, and multi-modal data.

· Preprocess, normalize, and feature-engineer datasets for SAM and other model types.

· Translate research models into production pipelines with RAG augmentation.

5. Front-End & Back-End AI Integration

· Extend AI models into front-end applications (interactive AI features).

· Integrate AI models into back-end services for low-latency inference.

· Ensure seamless integration between AI models and application architecture.

6. Production AI Infrastructure & Deployment

· Deploy AI models on MCP / GPU servers, distributed clusters, or cloud infrastructure.

· Manage staging, dev, and production environments, including SSH deployment, editing, and version control.

· Implement monitoring and observability using Prometheus, Grafana, and custom dashboards.

· Maintain CI/CD for AI models, training pipelines, and data workflows, including Git/GitHub workflow management.

7. Research to Production Translation

· Bring experimental AI models into production-grade systems.

· Validate model performance, reliability, and safety.

· Implement reproducibility, versioning, and automated retraining pipelines.

8. Mentorship & Leadership

· Mentor junior AI engineers and researchers.

· Establish best practices for AI experimentation, deployment, and RAG/agentic architectures.

· Contribute to AI roadmap and enterprise AI strategy.

Required Experience

· 5+ years AI / Deep Learning / Research & Production experience.

· Mandatory: Hands-on experience in LLM training pipelines, including cloud GPU infrastructure, data labeling, preprocessing, training, and post-processing.

· Deep knowledge of Neural Networks, Transformers, LLMs, Vision Transformers, Diffusion Models, Agentic AI, RAG/self-RAG pipelines.

· Hands-on experience with SAM models, tabular data ML, and preprocessing pipelines.

· Experience managing staging, dev, and production environments with SSH deployment, editing, and Git/GitHub workflow.

· Strong Computer Vision and Image Processing skills.

· Experience with agent orchestration, LangChain, LangGraph, and protocol optimization.

· Proven problem-solving, critical thinking, and innovative AI solution delivery.

· Experience integrating AI into front-end and back-end systems.

Preferred Experience

· Experience with generative AI, diffusion models, multi-modal agents.

· Optimizing LLM memory, inference, and fine-tuning at scale.

· Experience in production RAG pipelines and knowledge-augmented AI systems.

Application Question(s):

• Are you available to work from 2:00 PM – 11:00 PM shift Monday to Friday with alternate Saturday?

• Are you comfortable working onsite from Bahria Phase 4, Rawalpindi?

• How many years of experience do you have as a AI Engineer??

Work Location: In person