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Metro location, popular AI Engineer title, and broad full-stack plus GenAI skill needs increase candidate competition.
GenAI and ML skills are transferable across industries but enterprise RAG and platform experience favor similar domains.
Explicit 0–2 year requirement plus mandatory Python/LLM exposure and full-stack skills filter applicants.
Design, develop, and maintain full-stack and GenAI-enabled applications using Python, FastAPI, React, LLM APIs, and modern AI techniques like prompt engineering and RAG pipelines.
Build AI-powered tools such as chatbots, automation workflows, intelligent dashboards to enhance engineering and business productivity.
Collaborate across teams on converting business needs into software features, supporting Docker-based deployment, CI/CD, and ensuring secure, responsible AI practices.
Bachelor’s degree in Computer Science, IT, Data Science, AI, Engineering, or related technical field.
0–2 years of professional or equivalent hands-on experience in software development, focusing on full-stack, backend, or GenAI application development.
Practical knowledge of Python, REST APIs, web app development, Git/GitHub, and AI-assisted development tools like Cursor AI or GitHub Copilot.
Familiarity with LLMs, prompt engineering, RAG pipelines, embeddings, agentic AI patterns, plus backend frameworks like FastAPI or Django.
Early-career engineer comfortable working at the intersection of software engineering and AI/ML application development in a product-driven environment.
Hands-on developer with exposure to integrating and deploying large language model APIs and building AI-assisted productivity tools.
Collaborative and structured in approach, experienced in specification-driven development, agile SDLC, and secure, responsible AI implementation.