Enterprise LLM Engineer: Fine-Tuning & Deployment

Tech Mahindra; Abu Dhabi; On-site; AED 300,000 - 520,000; Full time

Job summary

Tech Mahindra seeks an experienced LLM Fine-Tuning & Model Engineer to adapt, evaluate, optimize, and deploy enterprise AI models. You will build robust training pipelines with LoRA/QLoRA and PEFT, evaluate performance, and scale deployments across cloud-native infrastruc­tures.

The role emphasizes collaboration with AI engineers and data scientists to ensure responsible AI, governance, and cost-aware optimizations in production environments.

Qualifications

• 4+ years of machine learning or AI engineering experience.
• Proven experience fine-tuning and deploying LLMs in production environments.
• Bachelor's or Master's degree in computer science, ML, AI or related field.

Responsibilities

• Fine-tune and optimize Large Language Models using domain-specific datasets.
• Design and implement training pipelines utilizing LoRA, QLoRA, PEFT, and SFT.
• Perform model evaluation, benchmarking, and performance analysis.
• Build and maintain RAG-based AI systems.
• Deploy and monitor models using Azure ML and cloud-native infrastructure.
• Optimize model performance, inference costs, latency, and scalability.
• Conduct experimentation around model distillation and alignment techniques.
• Collaborate with AI Engineers and Data Scientists to improve model quality.
• Ensure model governance, security, and responsible AI standards.

Skills

Python

NLP/LLMs

LoRA/QLoRA

PEFT

SFT

Hugging Face

Azure ML

RAG architectures

Vector databases

Education

Bachelor's or Master's in Computer Science/ML/AI

Tools

LoRA/QLoRA

PEFT

SFT

Hugging Face

Azure ML

Job description

Tech Mahindra seeks an experienced LLM Fine-Tuning & Model Engineer to adapt, evaluate, optimize, and deploy enterprise AI models. You will build robust training pipelines with LoRA/QLoRA and PEFT, evaluate performance, and scale deployments across cloud-native infrastruc­tures.

The role emphasizes collaboration with AI engineers and data scientists to ensure responsible AI, governance, and cost-aware optimizations in production environments.