Description

Key Skills: LLMs, AI Agents, RAG Systems, Vector Databases, Python, Machine Learning, Prompt Engineering, TensorFlow, PyTorch, Hugging Face Transformers

Good to Have Skills: Experience in building AI agents using graph-based architectures including knowledge graph embeddings and graph neural networks. Familiarity with LangGraph, CrewAI, or AutoGen frameworks. Experience with agent observability tools like Langfuse. Knowledge of FAISS, Pinecone, Weaviate, or Milvus. Experience with training small base models using custom data. Familiarity with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). Knowledge of programmatic advertising, RTB, or ad auction mechanics. Familiarity with MCP (Model Context Protocol) or similar tool-integration standards. Publications or contributions to research in AI, LLMs, or related fields.

Roles & Responsibilities:

• Provide technical leadership and mentorship to engineering teams while collaborating with architects, product managers, and UX designers to create innovative AI solutions that address complex customer challenges.
• Lead the design, development, and deployment of AI-driven features with end-to-end ownership from feasibility analysis to execution and release.
• Spearhead technical design meetings and produce detailed design documents that outline scalable, secure, and robust AI architectures.
• 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 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 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.
• Continuously monitor and optimize models to improve their performance, scalability, and cost efficiency.

Experience Required: 1 to 2 years of experience with strong understanding of LLMs and their underlying principles including 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

Key Skills

Education

Any Graduate