We are redirecting you to the source. If you are not redirected in 3 seconds, please click here.

Tech Lead (Platform Architect + Applied AI Engineer)- Digital Twin & Clinical Ai at Shae Group. Architect clinical AI and digital twin platforms — Senior Applied AI Lead Needed. Title: Tech Lead (Platform Architect + Applied AI Engineer)- Digital Twin & Clinical Ai - Remote (Contractor). Location: Remote - Global. - Philippines, Vietnam, Indonesia, China, Latin America (Mexico, Colombia, Brazil, Argentina, Peru, Ecuador), Eastern Europe (Poland, Ukraine, Romania, Bulgaria, Belarus, Lithuania), Africa (Kenya, South Africa, Nigeria, Ethiopia, Ghana, Egypt). Must have professional proficiency in spoken and written English.. Base fee: USD $1500 - $2,500 / MONTH + Performance Incentive (paid via direct deposit). Employment type: Contractor (full-time retainer). Company: Shae Group. Website:. https://shae.group. IF YOU SEE THIS POSTING THE JOB IS STILL ACCEPTING APPLICATIONS. Role Overview. Shae Group is hiring a senior Technical Solutions Architect / Tech Lead to drive architecture, technical direction, and delivery alignment for our clinical AI, conversational AI, and digital twin platform roadmap.. This role is AI-native: every Tech Lead at Shae must have hands-on AI engineering experience, especially in LLM prompt engineering and agentic/multi-agent systems shipped to production. We are not looking for candidates whose “AI experience” is limited to basic prompting or a single chatbot release.. You will act as the north star for technical execution: receiving product vision from leadership, translating it into technical architecture and documentation, keeping multiple teams aligned (including agency teams), and ensuring delivery stays on-track through strong communication, structured thinking, and rigorous quality standards.. What You’ll Own. 1) AI Engineering Leadership (Non-Negotiable). Lead delivery of LLM-driven, conversational AI systems (not rule-based chatbots). Build and ship production-grade capabilities such as:. Function calling / tool use / actions from conversation. Multi-agent / agentic workflows and orchestration. Prompt design patterns, testing, and evaluation approaches. Ensure systems are production-hardened with:. Robust testing and regression protection. Safety/guardrails and failure handling. Monitoring and performance controls. Preference weighting (per hiring manager):. Strongly prefer candidates with deep LLM prompt engineering experience (~70%). Fine-tuning experience is valuable but secondary (~30%); a few real projects can be sufficient. Bonus “brownie points”:. Experience deploying AI models/LLMs on the edge (edge inference / on-device / near-device constraints). 2) Platform Architecture & Cloud Systems (High-Level, Not Just App-Level). Own architectural thinking from the top down:. Cloud services, system boundaries, data flows, security controls. Design scalable foundations for:. Multi-tenant systems. White-label / configuration-driven product variants. Secure data ingestion pipelines (including wearables → processing → insights). Observability (logs, metrics, tracing), incident readiness, reliability standards. Define and enforce quality standards:. CI/CD expectations, linting, automated tests. “Definition of Done” for production readiness. 3) Technical Direction, Documentation, and Alignment. Convert vision into execution via strong written and visual artifacts:. Architecture diagrams. ADRs (Architecture Decision Records). Technical implementation plans and sequencing. Use AI tools (e.g., GPT) to accelerate:. Documentation drafting. Meeting note synthesis → action items. Ticket scaffolding and technical breakdowns. Candidates who refuse to use AI tooling for productivity are not a fit for this role.. 4) Delivery Leadership Across Multiple Teams. Act as the technical “brain” partnering closely with a Project Manager (PM):. PM handles admin/logistics; you ensure technical correctness + vision alignment. Drive team alignment through:. Tech lead presence in team syncs ~3x/week. On-demand calls with developers to unblock, clarify, and correct course. Ensure the right work is assigned to the right people:. Identify skill gaps, resourcing risks, or delivery threats early. Propose solutions, escalate for approval, and help roll changes out cleanly. Maintain delivery integrity across internal and agency teams:. Prevent drift from original vision. Ensure code is in the correct repo/codebase and meets quality expectations. What Success Looks Like. Teams ship complex AI features that work reliably in production (not prototypes). Architecture is documented clearly and used consistently by developers. Developers and PMs leave interactions with you feeling clear, calm, and confident. Projects stay aligned to vision, with minimal drift across distributed teams. Risks are surfaced early with structured options and clear recommendations. Delivery quality is high: CI/CD healthy, testing solid, releases controlled and safe. Required Skills & Experience (Must-Have). Senior-level engineering background (architecture + delivery leadership). AI engineering experience with evidence of shipping:. LLM-driven conversational systems. Agentic/multi-agent workflows. Production testing, evaluation, and hardening. Strong prompt engineering depth across multiple LLM providers/models. Ability to architect at a systems level:. Cloud services, security, data flows, reliability, observability. Strong software delivery discipline:. CI/CD, code quality, test strategy, release readiness. Excellent written communication:. Can produce structured, clear updates and decision docs quickly. Professional English fluency + strong accent comprehension. Must communicate clearly across multinational teams without repeated misunderstandings. Calm, firm, outcomes-driven leadership style:. Low ego, open-minded, but able to hold the line on standards and direction. Ability to context-switch effectively across multiple projects and threads. Preferred Skills (Nice-to-Have). Azure depth (especially Azure managed services). Machine learning background (helpful signal, not a strict requirement). Healthtech / regulated data environment experience. Edge AI / edge inference deployments. Voice agent experience, video integration, or advanced multimodal AI systems. Vector databases / RAG architectures for clinical decision-support workflows. Company Location: China.