Lead AI Engineer

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 AI Engineer?

Work Location: In person