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Remote role and metro hiring increase applicant density, but niche GenAI specialization limits breadth.
Requires specialized LLM, RAG, and productionization skills, so cross-industry transferability is limited.
Multiple mandatory technical requirements, production GenAI experience, and explicit years make filters strict.
Architect and implement scalable GenAI and Agentic AI solutions end-to-end, ensuring enterprise-grade reliability and performance on cloud platforms like Azure or AWS.
Convert business use cases into technical designs, define architectural guidelines, and review design documents to ensure best practices in scalability, security, and extensibility.
Write high-quality, production-ready Python code, build APIs with FastAPI and ORM integration, and scale AI solutions to support enterprise workloads.
6+ years total experience in AI/ML with proven production deployment of GenAI/Agentic AI solutions.
Deep understanding of LLMs (e.g., GPT, BERT-family) and transformer architectures with expert-level prompt engineering and experience in RAG patterns.
Strong proficiency in Python with extensive experience in AI/ML libraries (LangChain, Hugging Face Transformers, PyTorch/TensorFlow) and productionizing AI systems on Azure or AWS.
Bachelor’s or master’s degree in Computer Science, Information Technology, or related field.
Demonstrated ability to architect end-to-end GenAI solutions balancing functional and non-functional requirements with a focus on scalability and maintainability.
Experience collaborating across product and engineering teams to translate business needs into AI-driven solutions and perform systematic root cause analysis on issues.
Contributions to open-source GenAI projects and familiarity with advanced GenAI tooling (e.g., Model Context Protocol, vector databases, FastAPI, ORMs).