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Recognizable employer, mid-level ML role with broad LLM/MLOps skills creates high applicant competition.
Core skills in LLMs, Python, SQL, and MLOps are highly transferable across industries.
Advanced degree requirement, explicit 4–7 years, and deep Generative AI plus MLOps mandates raise strictness.
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Design and implement scalable AI and Generative AI solutions to enhance customer experience, business efficiency, and decision-making.
Build production-grade AI applications and workflows using Python, SQL, large language models, Retrieval-Augmented Generation, and agentic AI frameworks.
Collaborate with cross-functional teams to translate ambiguous business needs into practical AI solutions and communicate findings and recommendations clearly.
Master’s or Ph.D. degree in Data Science, Statistics, Engineering, Computer Science, Operations Research, or related STEM field.
4–7+ years of industry experience in AI engineering, data science, machine learning, or applied advanced analytics with production model deployment.
Advanced proficiency in Python, SQL, object-oriented programming, Git, CI/CD, and experience with large datasets in relational or distributed databases (e.g., Snowflake, BigQuery, SQL Server, Teradata).
Deep expertise in Generative AI including prompt engineering, LLM fine-tuning, embeddings, RAG, vector databases, agentic AI frameworks, and hands-on MLOps/LLMOps experience covering model monitoring, latency, drift mitigation, and system reliability.
Experienced in architecting and deploying production-grade Generative AI and advanced analytics solutions in enterprise settings.
Skilled in managing end-to-end AI lifecycle including model deployment, monitoring, and reliability in large-scale environments.
Proficient in advanced Generative AI techniques like multi-agent frameworks and Retrieval-Augmented Generation with strong software engineering and data engineering capabilities.