LLM Specialist

About the Role

We are seeking a skilled LLM Specialist with 2+ years of hands-on experience working with Large Language Models, NLP pipelines, prompt engineering, and model fine-tuning. The role involves building AI-driven solutions, optimizing model performance, developing conversational agents, and integrating LLMs into real-world applications.

You will collaborate with data scientists, ML engineers, and product teams to develop efficient, ethical, and scalable AI systems.

Key Responsibilities

1. LLM Development & Optimization

• Fine-tune and optimize large language models (GPT, Llama, Mistral, Falcon, etc.).
• Customize LLMs for domain-specific tasks: conversation, summarization, classification, content generation.
• Work on model evaluation, prompt design, and reinforcement feedback loops.

2. NLP Engineering

• Build NLP pipelines for text processing, entity recognition, sentiment analysis, and retrieval augmented generation (RAG).
• Implement embeddings, vector search, and semantic similarity models.

3. Prompt Engineering & Model Interaction

• Design effective prompts, system instructions, and multi-step workflows.
• Create reusable prompt templates for different use cases.
• Test, validate, and iterate prompts for accuracy and contextual alignment.

4. RAG Systems & Knowledge Integration

• Develop RAG pipelines using vector databases (Pinecone, Chroma, Weaviate, FAISS).
• Implement document ingestion, chunking, embeddings, and retrieval workflows.
• Enhance AI responses using structured + unstructured knowledge sources.

5. AI Integration & Deployment

• Integrate LLMs into backend systems, APIs, chatbots, and enterprise applications.
• Work with frameworks like LangChain, LlamaIndex, Haystack, or custom pipelines.
• Implement testing, monitoring, and performance optimization for deployed models.

6. Safety, Ethics & Compliance

• Apply responsible AI practices, bias detection, and output safety checks.
• Ensure models comply with data privacy, PII handling, and compliance standards.
• Conduct model red-teaming and robustness evaluations.

7. Collaboration, Documentation & Research

• Collaborate with product, engineering, and research teams to define AI features.
• Create documentation, model versions, datasets, and best practices.
• Stay updated with emerging LLM architectures, training techniques, and open-source tools.

Required Skills & Qualifications

Technical Skills

• Strong understanding of NLP, Transformers, embeddings, and LLM architectures.
• Experience with Python and libraries such as Hugging Face, Transformers, LangChain, Pydantic.
• Knowledge of vector databases (Pinecone, Chroma, FAISS, Weaviate).
• Ability to fine-tune and deploy models on GPU/Cloud setups.
• Familiar with ML frameworks: PyTorch, TensorFlow.
• Experience with API-based LLMs (OpenAI, Anthropic, Google, etc.).
• Understanding of evaluation metrics (BLEU, ROUGE, perplexity, accuracy).

Soft Skills

• Strong analytical and problem-solving ability.
• Clear communication and documentation skills.
• Ability to work cross-functionally and handle fast-paced environments.
• Creative mindset for building AI-driven products.

Preferred Qualifications

• Experience with RAG, agentic workflows, or AI automation tools.
• Exposure to GPU environments, model quantization, and optimization techniques.
• Understanding of data engineering workflows.
• Prior work on chatbots, summarization systems, or enterprise AI tools.