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Metro Bangalore, mid-level senior role and broad title increase applicant density, though GenAI specialization reduces it.
Role demands GenAI, LLMOps, and GCP-specific experience, making cross-industry transfers harder.
Multiple explicit requirements (5–8 yrs, GenAI experience, Vertex AI, Dataflow/Beam, LLMOps) create high shortlisting strictness.
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Design and implement production-grade data pipelines using Google Cloud Platform services such as Dataflow, Beam, and Vertex AI.
Develop and maintain GenAI and Agentic AI solutions including prompt engineering, RAG pipelines, embeddings, and agent components using ADK frameworks.
Ensure system reliability, cost optimization, security compliance, and mentor junior engineers while participating in code reviews and architectural discussions.
5–8 years in software/data/ML engineering with 1–2 years specifically in GenAI/agentic systems.
Hands-on experience with Google Cloud Platform AI stack including Vertex AI, BigQuery, Cloud Storage, Pub/Sub, Cloud Run, and Workflows.
Proficiency in Python, TypeScript, and Java; experience with Dataflow and Apache Beam or Spark is mandatory.
Experience implementing security controls (IAM, VPC-SC, Secret Manager) and compliance with Responsible AI principles.
Experienced in designing scalable, reliable AI-driven data pipelines on cloud-native architectures.
Skilled in applying LLMOps best practices including CI/CD pipelines, telemetry, and model lifecycle management.
Comfortable mentoring technical teams and collaborating closely with leadership on AI architecture standards and governance.