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Metro Hyderabad role with common senior title but niche agentic LLM skillset limits applicant density.
Role-specific LLM and marketing analytics expertise reduces transferability across unrelated industries.
Multiple mandatory technical requirements, explicit experience minima, and domain-specific LLM/ML-Ops skills make filters strict.
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Design, build, and operationalize production-grade agentic AI systems for marketing analytics, including multi-step tool-using agents and Retrieval-Augmented Generation (RAG) pipelines.
Own MLOps/LLMOps for AI systems: prompt versioning, model rollout, cost/latency monitoring, drift detection, and automated regression testing with CI/CD tools and cloud deployment.
Develop evaluation frameworks for accuracy, hallucination, and consistency of LLM outputs; integrate LLM APIs (Anthropic Claude, OpenAI) and build guardrails and human-in-the-loop review points.
At least 4 years of AI/ML experience, including minimum 2 years in Generative AI solutions.
Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn); experience with agentic AI engineering and LLM fine-tuning.
Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker/Kubernetes) for deploying ML/LLM pipelines.
Ability to build end-to-end GenAI MVPs with RAG/agents and evaluation harness; work with Claude Code, OpenAI Codex, and agentic workflow implementation.
Experienced in applied ML, data science, and production deployment of LLM/agentic AI with client-facing engagement and delivery focus.
Skilled in evaluation design, metrics, prompt engineering including structured output and tool use, and RAG pipeline optimization.
Practically oriented engineer who builds reliable, scalable AI systems using MLOps/LLMOps best practices, and integrates cloud-native GenAI tools into governed environments.