Machine Learning
Python Programming
Automation Scripting
Cloud Platforms
Problem Solving
API Integration
Process Optimization
Data Modeling
Agile Methodologies
Communication Skills
System Design
Robotic Process Automation
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
Lead end-to-end project delivery, from technical discovery and solution scoping through on-premise deployment and final handover.
Work hands-on with CCTV / video surveillance environments — IP cameras, NVRs, video management systems (VMS), and live video feeds — to plan and execute AI Vision deployments across client sites.
Plan and oversee on-premise server and software deployment: hardware sizing, Linux environments, containers (Docker), networking, and security considerations, in partnership with our engineers.
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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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