Senior Specialist, AI Engineer
Do meaningful work with us. Every day.At Amplify Health, we’re looking for individuals with ambition, resilience and passion for healthcare, insurance, wellness and digital technology. As a fast-growing business with the ambition of making people and communities across Asia healthier, we have exciting career opportunities available to help us achieve our vision.The Senior AI Engineer (Specialist) plays a pivotal role in designing, developing, and deploying GenAI/LLM, NLP and agentic AI solutions that deliver actionable insights across healthcare, insurance, and wellness domains. This individual collaborates with cross-functional teams, including data engineers, actuaries, clinicians, and product managers, to transform structured + unstructured healthcare data into LLM-enabled products (RAG copilots, summarization, extraction, triage, coding/abstraction, search, and agent workflows) with measurable reliability and safety.The role requires a blend of hands-on technical expertise, curiosity, problem solving and business acumen. The Senior AI Engineer is responsible for end-to-end delivery for AI workstreams from scoping to deployment/ monitoring; leads feature engineering strategy; mentors juniors and performs code reviews on top of being hands on. This role emphasizes engineering excellence: API/service design, testing, observability, release governance, and cost/latency optimization for LLM systems.The ideal candidate thrives in a fast-paced, agile environment and is passionate about leveraging data to solve real-world healthcare challenges.Responsibilities1) NLP/LLM Solution Architecture & Product DeliveryTranslate business workflows into NLP/LLM solution designs (RAG, classification, extraction, summarization, routing/triage, agents).Define what data is needed (first-/third-party, events, text, image, claims/transactions, IoT), data quality thresholds, and labelling strategy.Define north-star metrics (online and offline) and decision boundaries; craft counterfactuals and baselines (e.g., business-as-usual) to quantify impact. Connect model metrics to business outcomes.Own end-to-end delivery: design → build → test → deploy → monitor → iterate.Define system requirements including SLAs/SLOs, latency budgets, accuracy targets, cost ceilings, and safety constraints.Write and maintain AI System Design Specs (problem statement, users, decision loop, constraints, risk posture, evaluation plan, rollout strategy, and guardrails).2) LLM/NLP Development (Hands-on Build)Build RAG pipelines: corpus ingestion, chunking strategies, embedding selection, indexing, retrieval/reranking, grounding, citations, and fallback strategies.Develop prompt/tool schemas and agent designs: function calling, tool routing, memory patterns, and multi-step workflows. Apply modern NLP methods where appropriate: token classification, sequence labeling, semantic similarity, topic modeling, and hybrid IR (BM25 + dense retrieval).Ensure correctness through unit/integratio...