Hi there! 👋 Are you passionate about Artificial Intelligence and excited about building the next generation of AI-powered products? At Midorick, we're constantly connecting exceptional AI professionals with innovative companies across the US and LATAM. If you're looking for your next challenge and want to work on cutting-edge AI projects with global teams, we'd love to hear from you! 🔍 Who are we?Midorick Solutions is a global Employer of Record (EOR) that helps companies build high-performing remote teams across Latin America and the Caribbean. We connect top-tier talent with international organizations while taking care of everything from onboarding and payroll to local compliance, employee benefits, and HR support—making global hiring simple, fast, and compliant.

• Annotate medical records for AI training data • Validate annotated data to ensure quality • Refine the clinical logic behind AI outputs • Provide clinical coding & HIM subject-matter expertise to data science • Read and interpret clinical documentation — physician notes, assessment and plan sections, problem lists, medication records — to identify codeable diagnoses, conditions, and other clinical entities (document boundaries, type, author, section), applying ICD-10-CM and risk adjustment coding standards and mapping to clinical ontologies (ICD-10-CM/PCS, CPT, RxNorm) when required by project scope • Distinguish conditions that meet documentation standards for coding from those that do not, exercising clinical judgment independently, and flag ambiguous or edge-case documentation with written rationale • Review AI model output labels against clinical documentation to identify false positives, false negatives, and specificity errors; clean and correct label datasets and categorize error patterns for the data science team • Apply coding knowledge to evaluate whether model-generated code assignments are clinically and regulatorily supportable, and escalate systematic quality issues that may indicate model behavior problems • Translate ICD-10-CM and coding guideline requirements into explicit, testable instructions — LLM prompt language and computable coding rules — using AI-assisted tools testing revisions against curated ground-truth datasets and iterating on observed failures • Document the clinical rationale and precision/recall impact of each prompt or rule change for senior review

• Build the foundation. Stand up and run the shared AI infrastructure — integrations, data boundaries, the approved-model registry, and a standard AI stack with single sign-on and data-loss prevention that every function can build on. • Set the guardrails. Charter and run the governance council, and put in place the security, privacy, approval, and quality monitoring that let teams move fast without creating risk. • Own the roadmap. Set AI COE priorities in collaboration with other departments • Prove the impact. Define the standard metrics for cost, throughput, and quality, and report progress through the AI efficiency scorecard. • Build capability. Create centralized training libraries and enablement programs along with HR team, and build the place where teams share what works so the whole company benefits. • Lead the operating model. Run a centralized-expertise, decentralized-execution model where the Center of Excellence owns the system, functions own the outcomes • Build and grow the team. Hire, coach, and develop the Center of Excellence team, including forward-deployed AI engineers, admins etc

• Define and lead the data engineering, AI engineering, and core data services strategy to ensure scalability, efficiency, and alignment with business objectives. • Manage end-to-end data lifecycle from multiple sources (IoT, field data, manufacturing and production systems), including engineering, modeling, deployment, monitoring, and data operations. • Lead global teams responsible for data architecture, data engineering, integration, and data operations. • Ensure AI environments meet security, performance, availability, compliance, and cost-optimization requirements. • Promote global convergence of data and AI capabilities, tools, engineering best practices, and operational processes. • Oversee global monitoring, incident and problem management, capacity planning, disaster recovery, and lifecycle management.