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Specialized GenAI skills reduce applicant density despite a recognizable global brand.
Technical GenAI skills are transferable, but enterprise governance and Azure specificity increase domain sensitivity.
Multiple mandatory GenAI frameworks, Azure expertise, and explicit years requirement raise filtering strictness.
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Architect and deliver AI-driven solutions including document interpretation, automated decision flows, feedback generation, dashboards, and approval management.
Lead development of agent-based workflows using LLMs, retrieval pipelines, and multi-agent orchestration frameworks.
Design and optimize composite AI architectures combining language models, search, business rules, embeddings, analytics, and build backend components with API integrations across Azure AI services.
3–5+ years of AI engineering experience, ideally within large organizations or enterprise platforms.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical discipline.
Strong proficiency in Python and experience with Azure AI services and cloud architecture.
Experience designing, developing, and deploying Generative AI solutions using LLMs (e.g., GPT, Llama, Claude) and familiarity with GenAI frameworks such as LangChain, LlamaIndex, or Hugging Face Transformers.
Proven track record of leading AI/GenAI initiatives from concept through deployment in enterprise environments.
Operates effectively at the intersection of AI technology and business workflows, collaborating with stakeholders to translate needs into AI solutions.
Deep expertise in agentic design, multi-agent coordination, retrieval-based systems, cloud-native architecture, and DevOps practices, with ability to mentor and build high-performing teams.