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Known employer and popular AI title increase applicants, but senior requirement limits candidate pool.
Specialized LLM, RAG, and agentic AI expertise favors ML-specific backgrounds, reducing cross-industry transferability.
Explicit 8+ years and mandatory GenAI, Python, cloud, integration, and deployment skills make filters stringent.
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Design, develop, and deploy scalable AI solutions involving Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks.
Build and maintain production-grade Python backend systems with REST APIs, microservices, and enterprise integrations.
Implement CI/CD pipelines, monitor AI application performance, and ensure security and data governance compliance.
8+ years of software engineering experience focusing on Python development.
Hands-on experience with GenAI implementations, RAG architectures, AI agents, and LLM integrations in production.
Proficiency in developing REST APIs, microservices, and integrating AI solutions with enterprise platforms.
Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or related field.
Experienced in cloud-native AI application development using AWS, Azure, or GCP with containerization and Kubernetes.
Skilled in working with AI orchestration frameworks like LangChain, LangGraph, CrewAI, or similar technologies.
Capable of collaborating with stakeholders to deliver enterprise-scale AI applications and adapt in fast-paced environments.