Build LLM-based solutions for incident reports and remediation suggestions.
Leverage retrieval-augmented generation (RAG) to surface docs, logs, and KB insights.
Develop semantic search and context-aware retrieval for IT data.
Scale AI models for real-time, cloud-native deployment (AWS, GCP, Kubernetes).
Collaborate with platform engineers, product teams, and data scientists.
Maintain AI-first observability features to improve AI-driven automation.

🎯 Requirements

2-3 years in AI/ML with generative AI, AI-driven automation, and NLP.
Strong proficiency in Python, Java, or similar languages for model development.
Hands-on experience with LLMs, RAG, and prompt engineering.
Embeddings-based search, semantic retrieval, and transformers (OpenAI, HuggingFace).
Microservices, REST APIs, and cloud platforms (AWS, GCP, Kubernetes, Docker).
Observability/IT Ops or monitoring experience is a plus.

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