10+ years of experience in data architecture, distributed systems, or ML platform engineering.
Deep experience building on-premises or hybrid data systems (e.g., Spark, Kafka, Trino, Iceberg, Airflow).
Strong understanding of LLM architecture, embeddings, vector search, and agentic workflows.
Proven experience designing and implementing ML pipelines, inference runtimes, and data retrieval frameworks.
Proficiency with at least one programming language (e.g., Python, Go, Java) and AI / ML frameworks (e.g., LangChain, Haystack, Hugging Face Transformers).
Demonstrated expertise in designing and building scalable, production-grade RESTful APIs, enabling seamless integration across distributed systems and AI-powered applications.
Preferred :
Experience working with LLM orchestration frameworks, retrieval-augmented generation (RAG), and prompt engineering strategies.
Familiarity with open-source projects like LangGraph, AutoGPT, CrewAI, and ReAct-based agent architectures.
Strong communication skills and a track record of delivering complex systems in fast-paced environments.