We seek a Lead AI Engineer specializing in prompt engineering and optimizing large language models (LLMs) for real-world applications. In this role, you will not only design effective prompts, improve model responses, and build AI agents, but also take the lead in shaping and delivering the initial AI-powered product for the company. You'll be responsible for driving the technical vision, building from scratch, and leading the implementation of AI-driven capabilities that will become a core part of our product offering.
Responsibilities:
Prompt Engineering: Develop, test, and optimize prompts for high-quality AI responses across different tasks.
Retrieval-Augmented Generation (RAG): Implement RAG-based solutions to improve AI responses using external knowledge bases.
Integration & API Development: Work with software engineers to embed AI features into our product via APIs.
AI Agent Development: Design, build, and deploy autonomous AI agents that can reason, and execute multi-step tasks using LLMs, tools, memory, and APIs.
Experimentation & Evaluation: Continuously refine models using A/B testing, human feedback loops, and AI evaluation metrics.
Stay Updated: Keep up with the latest LLM advancements and best practices in the AI industry.
Product applications: Apply LLMs and agents to real-world scenarios that solve meaningful user problems.
Product implementation: Work closely with the product team to secure delivery is aligned with expected product requirements
Response validation: Implement a process to ensure accurate KPI measurement and consistent, expected outcomes.
Responsibilities:
Prompt Engineering: Develop, test, and optimize prompts for high-quality AI responses across different tasks.
Retrieval-Augmented Generation (RAG): Implement RAG-based solutions to improve AI responses using external knowledge bases.
Integration & API Development: Work with software engineers to embed AI features into our product via APIs.
AI Agent Development: Design, build, and deploy autonomous AI agents that can reason, and execute multi-step tasks using LLMs, tools, memory, and APIs.
Experimentation & Evaluation: Continuously refine models using A/B testing, human feedback loops, and AI evaluation metrics.
Stay Updated: Keep up with the latest LLM advancements and best practices in the AI industry.
Product applications: Apply LLMs and agents to real-world scenarios that solve meaningful user problems.
Product implementation: Work closely with the product team to secure delivery is aligned with expected product requirements
Response validation: Implement a process to ensure accurate KPI measurement and consistent, expected outcomes.
Requirements:
+4 years of experience as an AI and/or prompt engineer (OpenAI API, Anthropic Claude, etc.).
Proven experience building AI agents using frameworks like LangChain, AutoGPT, or custom stacks
Proficiency in Python and AI frameworks (PyTorch, LlamaIndex).
Deep understanding of tool use, memory, and autonomous task design for agents
Familiarity with embedding models and vector databases (FAISS, Pinecone, Weaviate).
Experience working with NLP pipelines, tokenization, and text preprocessing.
Strong analytical and problem-solving skills.
Strong business acumen with a proven ability to translate strategic objectives into actionable initiatives.
Preferred Qualifications:
Experience with cloud platforms (AWS, GCP, Azure) for AI deployment.
Previous experience integrating AI into production systems
Knowledge of MLOps best practices and CI/CD pipelines for AI models.
MSc Machine Learning & Data Science.
+4 years of experience as an AI and/or prompt engineer (OpenAI API, Anthropic Claude, etc.).
Proven experience building AI agents using frameworks like LangChain, AutoGPT, or custom stacks
Proficiency in Python and AI frameworks (PyTorch, LlamaIndex).
Deep understanding of tool use, memory, and autonomous task design for agents
Familiarity with embedding models and vector databases (FAISS, Pinecone, Weaviate).
Experience working with NLP pipelines, tokenization, and text preprocessing.
Strong analytical and problem-solving skills.
Strong business acumen with a proven ability to translate strategic objectives into actionable initiatives.
Preferred Qualifications:
Experience with cloud platforms (AWS, GCP, Azure) for AI deployment.
Previous experience integrating AI into production systems
Knowledge of MLOps best practices and CI/CD pipelines for AI models.
MSc Machine Learning & Data Science.
This position is open to all candidates.
