Job Summary:
We are seeking a highly skilled and creative AI Engineer with strong expertise in Generative AI, Large Language Models (LLMs), and Prompt Engineering. The role focuses on designing, developing, and optimizing intelligent AI-driven systems that enhance automation, improve user interactions, and deliver scalable business solutions.
The ideal candidate will have a deep understanding of transformer architectures, prompt design, and RAG (Retrieval-Augmented Generation) pipelines, with hands-on experience building and deploying models using platforms like OpenAI, Anthropic, Hugging Face, and Azure OpenAI.
Key Responsibilities:
- Design, test, and refine prompts to optimize performance of LLMs such as GPT-4, Claude, Gemini, and open-source models.
- Collaborate with cross-functional teams (product, design, engineering, marketing) to develop AI-driven features and tools.
- Conduct experiments and evaluate to test the accuracy, safety, and quality of AI-generated outputs, learning best practices along the way.
- Develop context-aware prompts, multi-turn dialogues, and dynamic prompt chaining for diverse applications.
- Build prompt libraries and templates for different business use cases (search, summarization, code generation, Q&A).
- Conduct A/B testing to assess model accuracy, creativity, factual reliability, and bias mitigation.
- Automate prompt workflows and integrate prompt chains with APIs and tool stacks.
- Fine-tune models or build retrieval-augmented generation (RAG) pipelines when needed.
- Stay curious and keep learning about the latest advancements in generative AI, NLP, and prompt engineering through hands-on practice and research
Required Skills & Qualifications:
- Bachelor’s degree in computer science.
- 2 years of experience working with LLMs, AI systems, NLP frameworks.
- Strong understanding of transformer models and training.
- Experience using OpenAI (ChatGPT, GPT API), Anthropic, Cohere, Hugging Face, or similar platforms.
- Strong proficiency in Python, with hands-on experience using leading AI frameworks and libraries such as LangChain, LlamaIndex, and others commonly used in LLM and NLP development.
- Familiarity with prompt tuning, zero-shot and few-shot learning, and LLM evaluation.
Background in UX, technical writing, or content design is a plus.