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Tier-1 brand, metro location, and mid-level experience increase applicant density despite niche generative-AI focus.
Generative-AI, agentic systems, and GCP MLOps specialization limit cross-industry transferability.
Mandatory years, LLM production experience, GCP and MLOps requirements make filtering highly selective.
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Design, develop, and deploy autonomous and semi-autonomous AI agents and multi-agent orchestration systems for enterprise business processes.
Build and maintain production-grade MLOps infrastructure on Google Cloud Platform including Kubernetes, Cloud Run, Terraform, and CI/CD pipelines.
Implement advanced memory management, prompt optimization, evaluation metrics, and ensure secure integrations with enterprise systems and APIs.
Minimum 3 years professional experience in Machine Learning or AI Engineering with strong MLOps foundation.
1-2 years hands-on experience designing and implementing LLM-based, generative AI or agentic systems in production.
Expert-level Python and experience with ML frameworks like PyTorch, TensorFlow, or Scikit-learn.
Proven experience with Google Cloud Platform (GCP), containerization (Docker, Kubernetes/GKE), Terraform, CI/CD tools, vector databases, REST/GraphQL APIs, and monitoring tools.
Experienced in architecting complex multi-agent AI systems using frameworks like LangChain, LangGraph, LlamaIndex, CrewAI, or Vertex AI Agent Builder.
Proficient in integrating and optimizing high-throughput RAG pipelines and semantic vector search technologies for enterprise-grade solutions.
Capable of translating complex operational requirements into scalable, secure, and cost-efficient AI deployments while maintaining documentation and stakeholder communication.