Agentic AI Engineer

Daily tasks: - Agentic Systems & Tool Design: Architect, build, and optimize autonomous AI agents, tool integration frameworks, skills, and multi-step agentic workflows. - Context Engineering & RAG: Design and refine advanced Retrieval-Augmented Generation (RAG) pipelines, system prompts, and context-window strategies for maximum accuracy and efficiency. - AI Research & Innovation: Stay at the forefront of cutting-edge AI/LLM research and translate emerging methodologies into practical product enhancements. - Model Evaluation & Error Analysis: Conduct systematic model selection, benchmarks, and granular error analysis to continuously improve output quality and mitigate hallucinations. - LLMOps & Observability: Deploy, monitor, and maintain LLM applications in production; implement telemetry, logging, and performance tracking to ensure system reliability. - Software Engineering: Write scalable, clean, and maintainable code, maintaining repository best practices and version control workflows. Programming Proficiency: High proficiency in Python , SQL , and Git version control. Agentic AI Expertise: Proven experience in prompt engineering, context engineering, RAG patterns, and designing agent/tool ecosystems. Evaluation & Optimization: Hands-on experience with LLM evaluation frameworks, benchmark design, model selection, and systematic error analysis. Production Deployment & LLMOps: Practical experience deploying LLM applications, including observability, tracing, cost monitoring, and latency tuning. Research-to-Product Mindset: Demonstrated ability to dissect recent AI papers and open-source breakthroughs and apply them directly to software products. Must have: Python, SQL, Git, AI, RAG, LLM