LLM Engineer

Position Overview

We are seeking a highly skilled LLM (Large Language Model) Engineer with a minimum of 2 years of hands-on experience in developing and deploying AI/ML solutions. The ideal candidate should have strong foundational and practical knowledge in Machine Learning, Deep Learning, and extensive experience working with Transformer models, RAG architectures, and fine-tuning methodologies. This role involves building, optimizing, and maintaining AI-driven applications that leverage state-of-the-art language models.

Key Responsibilities1. LLM Development & Optimization

• Design, develop, and optimize Large Language Model–based solutions for various business use cases.
• Implement and fine-tune Transformer-based architectures (GPT, BERT, LLaMA, T5, etc.).
• Develop custom LLM pipelines including Instruction tuning, Supervised Fine-Tuning (SFT), LoRA/QLoRA, and parameter-efficient tuning techniques.

2. RAG (Retrieval-Augmented Generation) Systems

• Build end-to-end RAG pipelines integrating vector databases (FAISS, Pinecone, ChromaDB, Weaviate).
• Implement document chunking, embedding strategies, and knowledge retrievers.
• Optimize retrieval latency, context relevance, and grounding accuracy.

3. Machine Learning & Deep Learning Expertise

• Develop ML/DL models and algorithms from scratch as needed.
• Perform model training, evaluation, hyperparameter tuning, and performance benchmarking.
• Utilize deep learning frameworks such as PyTorch and TensorFlow.
• Work with distributed training frameworks (DeepSpeed, HuggingFace Accelerate, Ray).

4. Data Engineering for LLM Pipelines

• Prepare high-quality datasets for fine-tuning, pre-training, and evaluation.
• Build automated data pipelines for preprocessing, labeling, augmentation, and validation.
• Ensure data governance, security, and compliance.

5. Deployment & MLOps

• Deploy LLMs into production environments using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
• Implement model versioning, CI/CD pipelines, and monitoring (MLflow, Weights & Biases).
• Optimize models for inference using quantization, pruning, and other efficiency techniques.

6. Cross-functional Collaboration

• Work closely with product teams, data scientists, and backend engineers.
• Convert business requirements into scalable AI-driven solutions.
• Participate in code reviews, design discussions, and documentation.

Required Skills & Qualifications

• 2+ years of experience as a Machine Learning Engineer, Deep Learning Engineer, or LLM Engineer.
• Strong understanding of ML algorithms, deep learning architectures, and NLP techniques.
• Hands-on experience with:
• Transformers and HuggingFace ecosystem
• RAG architectures
• Model fine-tuning and parameter-efficient tuning
• Proficiency in Python and ML/DL libraries (PyTorch, TensorFlow, HuggingFace Transformers).
• Experience with vector databases and embedding models.
• Strong understanding of LLM evaluation metrics (BLEU, ROUGE, perplexity, hallucination checks).
• Knowledge of cloud platforms (AWS/GCP/Azure) and containerization tools (Docker/K8s).
• Experience in productionizing ML models (MLOps).

Preferred Qualifications (Good to Have)

• Experience with LLM agent frameworks (LangChain, LlamaIndex, OpenAI Assistants API).
• Knowledge of GPU/TPU optimization, distributed training, and model parallelism.
• Experience with synthetic data generation and prompt engineering.
• Exposure to security and compliance in AI applications.

Soft Skills

• Strong analytical and problem-solving skills.
• Ability to work independently and in cross-functional teams.
• Excellent communication and documentation skills.
• High attention to detail and strong ownership mindset.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related fields.

Job Type: Permanent

Pay: From ₹40,000.00 per month

Benefits:

• Flexible schedule
• Health insurance
• Provident Fund

Work Location: In person