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Strong employer brand and metro location but specialized GenAI skillset reduces applicant density.
GenAI specialization with scientific integrations reduces cross-industry portability despite transferable ML/AI skills.
Explicit 10+ years and many mandatory GenAI, LLM, and infrastructure skills create strict filters.
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Lead architectural design and technical leadership for enterprise-grade AI and Generative AI platforms across multiple teams.
Own system design decisions, reference architectures, and implementation of AI-powered capabilities including LLMs, RAG, and agentic workflows for internal and external applications.
Mentor engineers, influence platform strategy, and ensure AI-driven systems are secure, scalable, resilient, and production-ready with measurable impact on scientific workflows and customer outcomes.
Bachelor’s degree in computer science, engineering, or related technical field (Master’s preferred).
10+ years industry experience in software engineering with AI solutions; includes production-grade AI/ML integration experience.
5+ years hands-on experience building scalable backend systems with Python and REST APIs (FastAPI preferred).
Proficiency with Git workflows, CI/CD, containerization (Docker, Kubernetes), and experience integrating LLMs via Azure OpenAI, Anthropic Claude, or OpenAI-compatible APIs.
Experienced in end-to-end AI system architecture and hands-on design of GenAI, RAG, and agentic AI workflows using tools like LangChain, LangGraph, and vector search technologies.
Demonstrated ability to lead cross-team architectural initiatives and mentor engineers within agile environments.
Strong backend development and data engineering skills coupled with experience in cloud-native, event-driven, and API-first architectures for complex AI-driven scientific applications.