Large Language Model Architect

Project Role : Large Language Model Architect
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Large Language Models (LLMs)
Good to have skills : NA
Minimum 18 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
Experienced in designing, developing, and deploying applications powered by Large Language Models (LLMs). Skilled in integrating foundation models into enterprise solutions, implementing prompt engineering techniques, optimizing model performance, and building AI-powered conversational applications while adhering to responsible AI and security best practices.

Roles & Responsibilities:
- Design and develop applications leveraging Large Language Models (LLMs) for various business use cases.
- Create, test, and optimize prompts to improve model accuracy, consistency, and response quality.
- Integrate LLMs with enterprise applications using APIs, SDKs, and orchestration frameworks.
- Develop Retrieval-Augmented Generation (RAG) solutions by integrating LLMs with vector databases and enterprise knowledge sources.
- Evaluate, monitor, and optimize model performance, latency, and cost.
- Implement AI governance, security, privacy, and responsible AI practices.
- Collaborate with cross-functional teams to identify AI use cases and deliver scalable solutions.
- Troubleshoot and resolve issues related to model outputs, integration, and production deployment.
- Stay up to date with advancements in foundation models, AI frameworks, and generative AI technologies.

Professional & Technical Skills
- Strong understanding of Large Language Models, Generative AI, and transformer-based architectures.
- Experience with prompt engineering, prompt optimization, and prompt evaluation techniques.
- Knowledge of Retrieval-Augmented Generation (RAG), embeddings, vector databases, and semantic search.
- Proficiency in Python and AI/ML libraries such as LangChain, LlamaIndex, Hugging Face Transformers, or similar frameworks.
- Experience with LLM APIs such as OpenAI, Anthropic, Google Gemini, or open-source models.
- Familiarity with model evaluation, fine-tuning concepts, inference optimization, and observability.
- Understanding of REST APIs, cloud platforms (Azure, AWS, or Google Cloud), and containerization technologies.
- Knowledge of responsible AI, data privacy, model security, and governance best practices.
- Strong analytical, problem-solving, communication, and collaboration skills.