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Strong employer brand plus sought-after GenAI skills, but senior specialization limits broad applicant pools.
Highly specialized GenAI, agentic AI, and LLMOps skillset makes cross-industry transferability limited.
Explicit 8+ years requirement and many mandatory LLM, cloud, and deployment skills create strict filters.
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Lead development and implementation of Agentic AI and GenAI solutions focusing on conversational agents, autonomous workflows, multi-agent systems, and enterprise automation leveraging tools like LangChain and LangGraph.
Design, build, and optimize cloud-native AI services on Azure, ensuring scalability, security, observability, and operational readiness including containerization with Kubernetes and CI/CD pipelines.
Drive ML/AI innovation through prototyping, evaluation frameworks, performance optimization, and lead technical teams providing mentorship and engineering best practices for GenAI adoption.
Bachelor's or Master's in Computer Science, Engineering, AI/ML, or related technical field; equivalent practical experience considered.
Minimum 8 years of experience in software engineering, AI/ML platform engineering, or large-scale distributed systems development.
Hands-on expertise with Agentic AI frameworks (e.g., LangChain, LangGraph), RAG pipeline design, Python programming, and cloud-native Azure services (Azure OpenAI, AKS, Cosmos DB, etc.).
Experience with containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, observability frameworks, and production-grade AI/ML application deployment.
Experienced in leading AI engineering teams with skills in Agentic AI architecture, prompt engineering, multi-agent collaboration, and complex AI system design for production environments.
Strong operational engineering focus with proficiency in cloud-native development on Azure, DevOps, MLOps/LLMOps, automated testing, and monitoring of AI solutions.
Background in innovation-driven roles or platform engineering teams that build reusable AI infrastructure and enterprise-grade conversational agents or AI workflow automation.