Newpage Solutions is a global digital health innovation company helping people live longer, healthier lives. We partner with life sciences organisations which include, pharmaceutical, biotech and healthcare leaders, to build transformative AI and data driven technologies addressing real-world health challenges.

From strategy and research to UX design and agile development, we deliver and validate impactful solutions using lean, human-centered practices.

We are proud to be a ‘Great Place to Work®’ certified company for the last three consecutive years. We also hold a top Glassdoor rating and are named among the "Top 50 Most Promising Healthcare Solution Providers" by CIOReview. As an organisation, we foster creativity, continuous learning and inclusivity, creating an environment where bold ideas thrive and make a measurable difference in people’s lives.

Your Mission

We are hiring Fullstack AI Engineers to build the systems that make AI products work in production. You will wire models, retrieval, tools, and data into something users can actually rely on—and you will own the backend, the integration surface, and the infrastructure underneath. You will also stand up prototype front-ends to make ideas tangible quickly, even though a front-end specialist will own the polished surfaces.

You treat AI as the substrate of how software gets built—not a tool to be cautious of, not something you are "exploring," but the medium you work in. You live at the current edge of AI development and work fluently in modern Python, comfortable enough in TypeScript to move across the stack, and at home with Claude Code, Cursor, agents, eval harnesses, and MCP as part of the daily toolkit.

This is a builder-first individual-contributor role. You will not wait for a refined backlog, a PM in the middle, or a separate platform team. You will pick up an AI capability, build it end-to-end, and stand it up in production yourself.

What You’ll Do

Build with AI

1. Build and ship the AI engine: retrieval-augmented generation, context-aware reasoning, evidence citation, and the evaluation harness around it.
2. Architect production-grade agentic applications using LangGraph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration layer.
3. Integrate frontier and self-hosted LLMs (Claude, GPT, Gemini, open-weight models) with tools, data, and external systems through MCP and custom connectors.
4. Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with ElasticSearch or Solr, BM25 + similarity search, re-ranking.
5. Maintain vendor-agnostic LLM abstractions so providers can be swapped behind a clean interface as enterprise constraints evolve.
6. Design prompt and context engineering frameworks that optimize accuracy, repeatability, cost, and latency.

Build the backend around it

1. Build modular backends in Python (FastAPI, async patterns) with comfort dipping into TypeScript/Node.js (Fastify, NestJS, Hono, Express) when the stack calls for it—aligned with clean architecture, OOP, SOLID, and domain-driven design.
2. Stand up prototype front-ends in Next.js, React, and TypeScript to make ideas tangible quickly knowing a front-end specialist will own the polished, production surfaces.
3. Design and ship REST APIs at scale, with OpenAPI/Swagger, webhook patterns, and clean integration boundaries.
4. Work across relational, document, key-value, and graph stores as the problem demands; use event-driven patterns where they fit, not by default.
5. Build enterprise integration surfaces: SSO (OAuth 2.0, OIDC, SAML), RBAC, ingestion pipelines from document and content systems, downstream tool connectors.
6. Implement audit trails, data classification, and change-history patterns where the use case requires them.

Ship, Operate, Harden

1. Spin up the infra, write the evals, wire the MCP servers, deploy the agents, and harden the bits that survive contact with real users.
2. Deploy on AWS (or Cloudflare for edge use cases) using containerization (Docker, Kubernetes, ECS, Fargate) or serverless (Lambda)—chosen for fit, not preference.
3. Own CI/CD end-to-end with GitHub Actions or equivalent; manage infrastructure as code with Terraform or Bicep.
4. Treat evals as a first-class discipline: hands-on harnesses, golden datasets, regression rubrics—not theoretical frameworks.
5. Apply engineering practices that hold up in production: TDD, secrets management and rotation, SAST/DAST, structured logging, metrics, tracing.
6. Use AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, Codex) through structured workflows, sub-agents, skills, and templates—with discipline and review.

What You Bring

1. 4+ years backend and AI engineering experience, production-grade.
2. Hands-on LLM integration experience: orchestration, RAG, vector stores, retrieval tuning, prompt versioning, evals.
3. Hands-on experience with agents, not just prompted models. You have wired tools to a model and let it run multi-step using LangGraph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration.
4. Strong Python with OOP, SOLID, 12-factor application development, and microservice architecture. You have built FastAPI services and similar.
5. Comfortable in TypeScript—enough to read it, ship in Node.js when needed, and stand up a Next.js + React prototype to make an idea tangible. You don't need to own polished front-end surfaces.
6. End-to-end implementation experience with vector databases, retrieval pipelines, and eval harnesses.
7. Enterprise integration experience: REST APIs at scale, OAuth/SSO, webhook patterns, ingestion from document and content systems.
8. Cloud-native AWS deployment experience—with Docker, Kubernetes, and GitHub Actions or equivalent. Cloudflare experience a plus.
9. Active, structured use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows, sub-agents, skills, and templates.
10. Comfort building for production environments where audit logs, data classification, RBAC, and change history matter.
11. A deep working understanding of how LLMs behave—and where they break—and how to optimize for accuracy, latency, and cost.
12. A no-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost.
13. A real, recent trail of built things: GitHub, a portfolio, side projects, indie tools, or OSS contributions.
14. A founder's mindset and genuine appetite for ambiguous, high-impact technical challenges.
15. Bachelor's or Master's in Computer Science, Machine Learning, or a related technical discipline.

Bonus Skills / Experience

1. DevOps depth: end-to-end infrastructure ownership, observability stacks (OpenTelemetry, Application Insights, Datadog), incident response.
2. Public writing, talks, or threads about building with AI.
3. MLOps and model serving experience (BentoML, MLflow, Vertex AI, SageMaker).
4. Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue).
5. Eval frameworks: Ragas, DeepEval, Promptfoo, LangSmith, or custom harnesses.
6. Healthcare or life sciences domain exposure.
7. AWS professional certifications or other relevant industry certifications.

What We Offer

At Newpage, we’re building a company that works smart and grows with agility, where driven individuals come together to do work that matters. We offer:

1. A people-first culture - Supportive peers, open communication and a strong sense of belonging
2. Smart, purposeful collaboration - Work with talented colleagues to create technologies that solve meaningful business challenges
3. Balance that lasts - We respect your time and support a healthy integration of work and life
4. Room to grow - Opportunities for learning, leadership and career development, shaped around you
5. Meaningful rewards - Competitive compensation that recognises both contribution and potential

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