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Metro location, popular AI title, and broad Generative AI skillset increase applicant competition.
AI engineering skills transfer across industries but require specific GenAI and ML systems expertise.
Explicit 0–2 years plus mandatory GenAI/LLM and production deployment experience increases filter strictness.
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Design, develop, and deploy AI solutions including Generative AI and Agentic AI frameworks to build intelligent, autonomous systems aligned with business needs.
Build data-driven applications and dashboards with high visibility using platforms like Power BI and Tableau, integrating AI into production environments including Azure and other clouds.
Manage structured and unstructured data pipelines using platforms such as Snowflake and collaborate across functions to embed AI into enterprise applications.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field.
0-2 years of experience or relevant internships/projects in AI or Data Engineering.
Proficient programming skills in Python, Java, or C# with experience using AI libraries like TensorFlow and PyTorch.
Experience with Generative AI (LLMs), Agentic AI frameworks (LangChain, Hugging Face, OpenAI APIs), and basic knowledge of cloud AI tools (AWS SageMaker, GCP AI, Azure ML).
Early career AI professional with practical exposure to Generative AI and Agentic AI systems and frameworks for production-grade applications.
Comfortable working with cloud platforms and data engineering tools like Snowflake, with ability to build end-to-end AI solutions including data pipelines and dashboards.
Skilled in integrating AI models into business contexts, collaborating cross-functionally to deliver AI-enhanced applications with real-time or visible business impact.