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.
Responsibilities
1) NLP/LLM Solution Architecture & Product Delivery
- Translate 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/integration tests, robust error handling, and deterministic behavior where needed.
- AI/ ML Accelerators development:
- Build and maintain reusable ML accelerators (Cookiecutter, Feature Engineering Toolkit, AutoML , Unified Evaluation Harness, Observability Blueprints, Responsible AI Pack etc) that standardize feature engineering, model training, and evaluation across tasks.
3) Evaluation, Quality, and Reliability (LLMOps)
- Build and maintain evaluation harnesses:
- Offline test sets, golden datasets, and regression suites
- LLM-as-judge where appropriate (with controls)
- Human-in-the-loop review loops for high-risk workflows - Define and track quality metrics: groundedness, faithfulness, toxicity/safety, extraction accuracy, retrieval precision/recall, and task success rates.
- Implement guardrails: policy filters, PHI/PII handling, prompt injection defenses, output constraints, and safe-completion behaviors.
4) Production Engineering, MLOps & Observability
- Productionize services using containerization and orchestration (e.g., Docker, Kubernetes) and CI/CD pipelines.
- Implement observability: structured logging, traces, prompt/version tracking, vector DB metrics, and cost monitoring.
- Monitor performance and drift signals; define retraining/re-indexing/re-prompting strategies and release governance.
- Optimize for performance and cost: caching, batching, streaming, quantization where relevant, and efficient retrieval.
5) Collaboration & Stakeholder Engagement
- Partner with cross-functional teams—including actuaries, clinicians, engineers, and product managers—to align technical solutions with strategic objectives.
- Facilitate technical workshops and presentations to ensure clarity and buy-in across diverse audiences.
- Act as a subject matter expert on analytics, data science methodologies and best practices.
6) Governance & Compliance
- Ensure adherence to data privacy regulations and implement security best practices across all data science workflows.
- Advocate for responsible AI by incorporating fairness, explainability, and bias detection into model development.
- Maintain comprehensive audit trails and documentation for regulatory compliance and internal governance.
Candidate Profile
Experience and Qualifications
- Bachelor’s or master’s degree in computer science, Engineering, Machine Learning, NLP, or related field.
- ~8–10 years of industry experience building production systems (with at least 2–3 years in NLP/LLM or applied ML engineering).
Technical Expertise
Programming & Data Foundations
- Strong proficiency in Python/ Pyspark (data wrangling, EDA, modeling) and SQL for working with large, complex datasets; advanced Excel for analysis and validation.
- Reproducible analytic workflows (modular code, notebooks, documentation) and robust data handling across heterogeneous sources.
- Analytical Rigor & Problem Solving
- Experience in defining evaluation taxonomies and acceptance criteria across initiatives; balances statistical and operational risk.
- Experience in codifing analytical playbooks and institutionalizes measurement frameworks across products/teams. Arbitrates trade-offs (accuracy, fairness, latency, interpretability) for high impact decisions.
Core AI & Generative AI Expertise
- Framework Mastery: Deep proficiency in Python and industry-standard machine learning frameworks such as PyTorch, Hugging Face, or TensorFlow.
- Advanced Architecture: Strong knowledge of neural network patterns, specifically Transformer architectures, Large Language Models (LLMs), and Small Language Models (SLMs).
- Agentic AI & Orchestration: Experience architecting multi-agent systems and expert routing using frameworks like LangChain, LangGraph, LlamaIndex, or CrewAI.
- RAG & Vector Data: Hands-on experience optimizing Retrieval-Augmented Generation (RAG) pipelines using vector databases such as Pinecone, Milvus, or Weaviate.
- Model Optimization. Expertise in fine-tuning, prompt engineering, hyperparameter tuning, and context-chaining techniques.
Software Engineering & MLOps Infrastructure:
- Production Engineering: Solid software development fundamentals, including clean architecture, version control (Git), writing automated unit/integration tests, and CI/CD pipelines.
- Cloud & Containerization: Experience hosting and scaling models on major cloud infrastructure platforms like AWS, GCP, or Azure using Docker and Kubernetes.
- LLMOps & Observability: Utilization of specialized monitoring tools (e.g., Langfuse, Weights & Biases, PromptLayer) to track model evaluation, latency, drifts, and token spend optimizations.
- Data Pipelines: Familiarity with structuring knowledge graphs, processing multi-modal data streams, and querying database engines.
Cloud & Data Platforms (Microsoft Azure)
- Experience with Azure Databricks, Data bricks, for scalable data processing, model training, and orchestration
Governance, Privacy & Responsible AI
- Knowledge of data privacy/security best practices across workflows.
- Knowledge of applying Responsible AI principles into model building, comprehensive documentation and audit trails for compliance experience.
- Experience in establishes documentation guidelines and review checkpoints
GenAI-first & Vibe Coding
- Experience in GenAI vibe-coding workflow by default (generate–refine–test–document), while maintaining code quality, reviews, and reproducibility.
- Experience in using Agentic AI/ GenAI tools to draft design specs, model cards, experiment summaries, runbooks, and to automate repetitive analysis/engineering tasks to drive measurable efficiency and productivity gains.
Competencies & Core Characteristics:
We are seeking professionals who embodies the following competencies and characteristics essential for success in our scale-up environment:
- Technical Domain Expertise (Modelling): Demonstrates mastery of data science methodologies, programming languages, and cloud-based AIML platforms. Applies advanced techniques to build robust, scalable models and data pipelines that meet business objectives.
- Analytical Rigor & Problem Solving: Demonstrates mastery of translating complex datasets into actionable insights using AI/ML algorithms. Maintains high standards of accuracy and validity in all outputs.
- Unifier & Cross-Functional Influencer: Influences roadmaps; aligns multiple teams toward shared model goals. Communicates effectively to bridge gaps between data science and business needs. Represents DS in cross org forums, shapes data strategy and methodological direction for the portfolio.
- Adaptable & Resilient Operator: Operates in high ambiguity; sequences investments; ensures teams deliver value at pace. Coach’s others in risk management and decision velocity. Delivers under uncertainty and changing scope; prioritizes pragmatically and manages risk to land outcomes at speed.
- Curiosity & Innovation: Proactively explores new methods and tools; runs lean experiments to separate signal from noise and codifies learnings.
- Responsible & Governed AI: Applies privacy-by-design, fairness, transparency, and model documentation practices; champions responsible AI guardrails.
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