LLM Engineer

LLM Engineer

designing and implementing at least two chatbot projects

Key Responsibilities

• Design, architect, and develop intelligent chatbot systems using state-of-the-art LLM techniques.
• Analyze business and functional requirements and translate them into scalable LLM solutions.
• Implement

  Retrieval-Augmented Generation (RAG)

  pipelines using relevant libraries and vector databases.
• Apply

  instruction fine-tuning

  and

  PEFT techniques

  like LoRA/QLoRA for domain-specific customization.
• Build LLM applications using frameworks such as

  LangChain

  ,

  LlamaIndex

  ,

  Haystack

  , or

  Rasa

  .
• Integrate chatbots with enterprise systems, APIs, and document stores.
• Work with

  vector databases

  (e.g., Pinecone, FAISS, Weaviate, ChromaDB) and embedding models.
• Collaborate cross-functionally with design, product, and engineering teams for integration and deployment.
• (Preferred) Develop

  multimodal systems

  that process combinations of text, audio, video, and images.
• Stay up to date with the latest research in conversational AI, LLMs, and multimodal AI.
• Document technical architecture, processes, and best practices.

Required Qualifications

• Bachelor's or Masters in Computer Science, Artificial Intelligence, Data Science, or equivalent.

• 6 to 9 years

  of total experience in software/AI development with

  minimum 2+ chatbot projects

  delivered using LLMs.
• Proficiency in

  RAG

  ,

  instruction tuning

  , and

  parameter-efficient fine-tuning (LoRA/QLoRA)

  techniques.
• Strong expertise in one or more frameworks:

  LangChain

  ,

  LlamaIndex

  ,

  Haystack

  ,

  Rasa

  , etc.
• Solid programming skills in

  Python

  and familiarity with

  Transformers

  ,

  PyTorch

  , or

  TensorFlow

  .
• Experience with

  vector databases

  ,

  embedding models

  , and

  NLP pipelines

  .
• Ability to understand business needs and independently drive solution strategy and implementation.
• Excellent communication and stakeholder engagement skills.

Preferred Qualifications

• Exposure to

  multimodal AI

  and

  Vision-Language Models (VLMs)

  such as Flamingo, GPT-4V, or Kosmos.
• Experience with MLOps tools and deploying LLMs into scalable production environments.
• Understanding of prompt engineering, few-shot learning, and performance optimization for LLMs.
• Familiarity with model evaluation metrics specific to conversational and generative tasks.

Soft Skills

• Strong analytical and problem-solving mindset.
• Self-motivated with the ability to take initiative and ownership.
• Effective communicator with the ability to translate complex technical concepts into business outcomes. Comfortable working in agile, fast-paced environments