Back
Mid-level Bangalore AI role with popular title but niche GenAI requirements creates moderate competition.
Specialized GenAI and GCP Vertex AI requirements increase domain specificity, limiting cross-industry portability.
Explicit 5–8 years plus mandatory GCP, GenAI, RAG and Python requirements make shortlisting stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and develop scalable backend services and APIs using Python and frameworks like FastAPI or Django.
Build, deploy, and maintain GenAI applications including 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 manage services via GCP components such as Cloud Run and Vertex AI.
5-8 years of software engineering experience with strong Python expertise.
Hands-on experience with backend frameworks like FastAPI and Django, and building RESTful APIs and microservices.
Proficient in Google Cloud Platform services specifically Vertex AI, Cloud Run, BigQuery, Cloud Storage, Dataflow, and PostgreSQL.
Familiarity with GenAI technologies including AI agents, RAG pipelines, LLMs, Google ADK, Model Context Protocol (MCP), and orchestration frameworks like LangChain or equivalent.
Experienced in developing production-grade AI/ML backend solutions with a focus on scalability and performance optimization on cloud-native infrastructure.
Comfortable working in Agile environments using tools like Jira and GitHub for version control, code reviews, and collaboration.
Capable of mentoring junior engineers and contributing to technical design and standards, demonstrating senior-level ownership of AI application lifecycles.