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Mid-level specialized ML role with popular LLM/GenAI skills but non-metro location yields moderate competition.
Role requires specific ML/GenAI and GCP experience making cross-industry transferability limited and domain-sensitive.
Multiple mandatory requirements (5–8 years, GCP/Vertex AI, RAG/LLMs, Python frameworks) indicate strict filters.
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Design and build scalable AI-powered backend services and APIs using Python frameworks such as FastAPI and Django.
Develop, deploy, and maintain GenAI applications including Retrieval-Augmented Generation (RAG) pipelines, AI agents, and LLM integrations on Google Cloud Platform.
Architect end-to-end AI solutions covering data ingestion, processing, model integration, deployment, and performance optimization using GCP services like Cloud Run, Vertex AI, BigQuery, and Dataflow.
5–8 years of software engineering experience with strong proficiency in Python.
Hands-on experience with backend frameworks FastAPI and Django.
Strong experience using Google Cloud Platform services including Vertex AI, Cloud Run, BigQuery, Cloud Storage, and Dataflow.
Experience with GenAI technologies including AI agents, RAG pipelines, LLMs, and orchestration frameworks such as LangChain or equivalent.
Experienced in production-grade AI system development focused on backend services and API design.
Skilled in designing scalable, cost-effective solutions on Google Cloud Platform with attention to cloud cost optimization and performance tuning.
Capable of mentoring junior engineers and driving technical design and standards in an Agile, collaborative environment using tools like Jira and GitHub.