Sr. AI Engineer - Large Language Models (Sahibzada Ajit Singh Nagar)

Job Title: Sr. AI Engineer - Large Language Models (LLMs)

Experience: 5+ years

Key Responsibilities

Fine-tuning LLMs

Develop, train, and fine-tune large language models to meet specific client and business requirements.

Perform domain adaptation using specialized datasets for improved model relevance and accuracy.

LLM Deployment

Design and implement scalable, resource-efficient deployments of LLMs for production environments.

Optimize model inference for low latency while maintaining high accuracy.

LLM Agent Development

Build intelligent agents leveraging LLMs to automate workflows and support complex tasks.

Integrate LLMs with external systems and APIs for enhanced functionality.

RAG Implementation

Implement retrieval-augmented generation (RAG) techniques to improve LLM performance on knowledge-intensive tasks.

Integrate document retrieval systems and knowledge bases into LLM workflows.

Collaboration & Communication

Work closely with cross-functional teams, including data scientists, engineers, and domain experts.

Present findings and technical designs to stakeholders in a clear and concise manner.

Qualifications

Bachelors or Masters degree in Computer Science, AI, Machine Learning, or a related field.

Proven experience in fine-tuning and deploying large language models (e.g., GPT, Llama, T5, etc.).

Expertise in optimization techniques for memory management and latency reduction.

Proficiency in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers).

Hands-on experience with deploying AI models in scalable architectures (e.g., Kubernetes, Docker, cloud platforms like AWS/GCP/Azure).

Knowledge of NLP and deep learning algorithms.

Preferred Skills

Experience with distributed training and fine-tuning of LLMs.

Familiarity with MLOps tools and pipelines.

Knowledge of publishing workflows and challenges in the research domain.

Robust understanding of retrieval-augmented generation (RAG) techniques and their applications.

Strong programming skills in Python or similar languages. .