• Lead the design, development, and deployment of AI-driven features. Drive end-to-end ownership—from feasibility analysis and design specifications to execution and release—while ensuring quick iterations based on customer feedback in a fast-paced Agile environment. 
• Spearhead technical design meetings and produce detailed design documents that outline scalable, secure, and robust AI architectures. 
• Ensure that the solutions are aligned with long-term product strategy and technical roadmaps. 
• Implement and optimize LLMs for specific use cases, including fine-tuning models, deploying pre-trained models, and evaluating their performance. 
• Develop AI agents powered by RAG systems, integrating external knowledge sources to improve the accuracy and relevance of generated content. 
• Design, implement, and optimize vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient and scalable vector search, and work on various vector indexing algorithms. 
• Create sophisticated prompts and fine-tune them to improve the performance of LLMs in generating precise and contextually relevant responses. 
• Utilize evaluation frameworks and metrics (e.g., Evals) to assess and improve the performance of generative models and AI systems. 
• Work with data scientists, engineers, and product teams to integrate AI-driven capabilities into customer-facing products and internal tools. 
• Stay up to date with the latest research and trends in LLMs, RAG, and generative AI technologies to drive innovation in the company’s offerings. 
• Continuously monitor and optimize models to improve their performance, scalability, and cost efficiency.

We'd Love for You to Have:

• 2 to 10 years of total experience and strong understanding of LLMs and their underlying principles — transformer architecture, attention mechanisms, and hyperparameter tuning.
• Proven experience designing and building AI agents, including multi-agent orchestration, tool-use patterns, multi-step planning, and agent memory architectures (short-term and long-term).
• Hands-on experience with agentic frameworks such as LangGraph, CrewAI, or AutoGen, and familiarity with RAG pipelines that integrate external knowledge sources (documents, databases, APIs).
• In-depth knowledge of vector databases and indexing algorithms; practical experience with FAISS, Pinecone, Weaviate, or Milvus.
• Experience with agent observability, tracing, and guardrails — tools like Langfuse or equivalent — to ensure reliability, safety, and debuggability of agentic systems.
• Proficiency in prompt engineering — crafting, iterating, and optimizing complex prompts for context-sensitive, domain-specific LLM outputs.
• Familiarity with Evals and other performance evaluation tools for measuring model quality, relevance, and efficiency. 
• Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and Hugging Face Transformers. 
• Experience with data preprocessing, vectorization, and handling large-scale datasets. 
  • Ability to present complex technical ideas and results to both technical and non-technical stakeholders.