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Metro location and broad senior data role increase competition, but GenAI specialization reduces applicant density.
Medium because core data engineering and GenAI skills transfer across industries despite MarTech preference.
High due to explicit 8+ years requirement and mandatory GenAI, LLM, cloud and vector database expertise.
Design and implement end-to-end Generative AI and LLM-based architectures integrated with enterprise data and applications, including scalable RAG workflows on cloud platforms.
Develop and deploy LLMs and custom AI models using frameworks like LangChain, LlamaIndex, and Hugging Face, alongside building data pipelines for structured and unstructured data.
Lead innovation by evaluating new AI/ML tools, mentoring teams, and ensuring scalability, reliability, and governance of AI and data pipelines.
8+ years of experience including hands-on data engineering and Generative AI solution development.
Bachelor’s or Master’s degree in Computer Science or a related field.
Proficiency with Python, SQL, PySpark, and data pipeline design (ETL/ELT) plus AWS cloud services (SageMaker, Bedrock, Lambda, API Gateway).
Proven expertise in Generative AI and LLM architectures, including frameworks like OpenAI, Anthropic, Hugging Face, LangChain, and LlamaIndex.
Strong background in enterprise-scale AI solution architecture with practical experience deploying cloud-native Generative AI systems.
Experienced in integrating various data types and ML pipelines with strong capabilities in vector search and semantic retrieval technologies.
Capable of communicating complex AI concepts effectively to technical and non-technical stakeholders, with leadership experience mentoring data scientists and engineers.