This role is responsible to assist with reviewing medical insurance cases. This support is critical to the success of our current projects, which involves leveraging methods and cleaning data from the big data platform.-provides analysis, reports and insights required for data management
-organizes data, analyzes and provides insights required-makes precise reviews with additional expertise support essential to meet deadlines without compromising quality-supports the overall objectives of the medical projects and addresses the issues of case review with divisional leaders which can foster personal growth
Commission new stores end to end — rack layout, structured cabling, router/firewall and managed switch configuration, VLAN segmentation (management / POS & token / IoT machines / CCTV / staff), access point setup, WireGuard tunnel back to HQ, Zabbix agent registered.
Bring the machine layer online — wire and address washer/dryer controllers, verify RS485/Modbus TCP connectivity, pull and run the Docker container for the IoT service, confirm the FastAPI endpoint is reachable and returning correct machine state, and prove each machine reads and writes before handover.
Diagnose faults to root cause — machines become offline, a token terminal drops off, a store loses its tunnel. You isolate it layer by layer (cable → link → IP → VLAN → route → firewall → service → application) and document what it actually was.
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Agentic AI ArchitectureDesign and implement production-grade agentic systems using models such as Claude. Agent orchestration, tool calling, function calling, multi-agent architectures, planning and task decomposition, agent memory, context management, state machines and workflow engines, long-running agents, human-in-the-loop systems, autonomous execution, recovery and retry mechanisms, observability, and evaluation. You understand the difference between LLM → Agent → Workflow → Autonomous System, and when each is appropriate.
Agentic Loops & Self-OptimizationA major part of the role is building closed-loop systems: Goal → Plan → Execute → Observe → Evaluate → Learn → Re-plan → Execute. Systems that evaluate their own outputs, detect failed actions, identify root causes, adjust strategies, optimize prompts and tool selection, keep what works, roll back what does not, and improve over time. Reflection, critique, self-evaluation, feedback loops, reward signals, evaluation frameworks, automated experimentation, memory, retrieval, state management.
Claude / LLM EngineeringDeep practical experience with Claude/Anthropic APIs is highly desirable. Tool use, structured outputs, streaming, context management, prompt engineering, system prompts, long-context workflows, model routing, token optimization, latency and cost optimization, context compression, agent memory, LLM evaluation. Experience with other frontier models (OpenAI, Gemini, Llama or equivalent) is a plus.
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