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About This Role
Location: Remote \| Type: Contract
About Newpage Solutions
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
- Build and ship the AI engine: retrieval\-augmented generation, context\-aware reasoning, evidence citation, and the evaluation harness around it.
- Architect production\-grade agentic applications using LangGraph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration layer.
- Integrate frontier and self\-hosted LLMs (Claude, GPT, Gemini, open\-weight models) with tools, data, and external systems through MCP and custom connectors.
- Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with ElasticSearch or Solr, BM25 \+ similarity search, re\-ranking.
- Maintain vendor\-agnostic LLM abstractions so providers can be swapped behind a clean interface as enterprise constraints evolve.
- Design prompt and context engineering frameworks that optimize accuracy, repeatability, cost, and latency.
Build the backend around it
- 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.
- 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.
- Design and ship REST APIs at scale, with OpenAPI/Swagger, webhook patterns, and clean integration boundaries.
- Work across relational, document, key\-value, and graph stores as the problem demands; use event\-driven patterns where they fit, not by default.
- Build enterprise integration surfaces: SSO (OAuth 2\.0, OIDC, SAML), RBAC, ingestion pipelines from document and content systems, downstream tool connectors.
- Implement audit trails, data classification, and change\-history patterns where the use case requires them.
Ship, Operate, Harden
- Spin up the infra, write the evals, wire the MCP servers, deploy the agents, and harden the bits that survive contact with real users.
- Deploy on AWS (or Cloudflare for edge use cases) using containerization (Docker, Kubernetes, ECS, Fargate) or serverless (Lambda)—chosen for fit, not preference.
- Own CI/CD end\-to\-end with GitHub Actions or equivalent; manage infrastructure as code with Terraform or Bicep.
- Treat evals as a first\-class discipline: hands\-on harnesses, golden datasets, regression rubrics—not theoretical frameworks.
- Apply engineering practices that hold up in production: TDD, secrets management and rotation, SAST/DAST, structured logging, metrics, tracing.
- 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
- 4\+ years backend and AI engineering experience, production\-grade.
- Hands\-on LLM integration experience: orchestration, RAG, vector stores, retrieval tuning, prompt versioning, evals.
- 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.
- Strong Python with OOP, SOLID, 12\-factor application development, and microservice architecture. You have built FastAPI services and similar.
- 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.
- End\-to\-end implementation experience with vector databases, retrieval pipelines, and eval harnesses.
- Enterprise integration experience: REST APIs at scale, OAuth/SSO, webhook patterns, ingestion from document and content systems.
- Cloud\-native AWS deployment experience—with Docker, Kubernetes, and GitHub Actions or equivalent. Cloudflare experience a plus.
- Active, structured use of AI\-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows, sub\-agents, skills, and templates.
- Comfort building for production environments where audit logs, data classification, RBAC, and change history matter.
- A deep working understanding of how LLMs behave—and where they break—and how to optimize for accuracy, latency, and cost.
- A no\-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost.
- A real, recent trail of built things: GitHub, a portfolio, side projects, indie tools, or OSS contributions.
- A founder's mindset and genuine appetite for ambiguous, high\-impact technical challenges.
- Bachelor's or Master's in Computer Science, Machine Learning, or a related technical discipline.
Bonus Skills / Experience
- DevOps depth: end\-to\-end infrastructure ownership, observability stacks (OpenTelemetry, Application Insights, Datadog), incident response.
- Public writing, talks, or threads about building with AI.
- MLOps and model serving experience (BentoML, MLflow, Vertex AI, SageMaker).
- Streaming and batch ingestion pipelines (Spark, Airflow, Beam, Glue).
- Eval frameworks: Ragas, DeepEval, Promptfoo, LangSmith, or custom harnesses.
- Healthcare or life sciences domain exposure.
- 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:
- A people\-first culture \- Supportive peers, open communication and a strong sense of belonging
- Smart, purposeful collaboration \- Work with talented colleagues to create technologies that solve meaningful business challenges
- Balance that lasts \- We respect your time and support a healthy integration of work and life
- Room to grow \- Opportunities for learning, leadership and career development, shaped around you
- Meaningful rewards \- Competitive compensation that recognises both contribution and potential
Ready to Apply?
Let’s build the future of health together. Apply below or reach out to: *[email protected]*
#### More about Newpage
Newpage is a digital health solutions company. We devote ourselves to advancing the quality of life by enhancing health and optimizing the longevity of people. We do this by, passionately building futuristic technologies for global organizations across the healthcare ecosystem. We partake at every stage from problem definition, strategy \& service design, user research, UX design, and agile software development – utilizing lean practices to deliver and validate highly innovative digital health solutions that drive user value and business transformation.
Newpage is recognized by ‘CIO’s Review’ as “Top 50 Promising Healthcare Solution Providers” and Great Place to Work Certified (GPTW) 2023 \& 2024\.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Newpage Digital Healthcare solutions, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Newpage Digital Healthcare solutions AI Hiring
Newpage Digital Healthcare solutions has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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