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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 seeking an experienced AI Tech Lead to join the Global Digital team of one of our clients. In this role you will set the technical direction for a fast\-moving AI build team, write production\-grade code, and turn bold ideas into real\-world products. You will work on high\-impact, ELT\-1 business cases, with direct exposure to the strategic priorities of senior leadership.
We are looking for an exceptional engineer who pairs deep software development experience with a passion for pushing the boundaries of what is possible with AI. The team thrives on experimentation: quick hacks in 24 to 48 hours, a working prototype in a week, and a production deployment the week after. You will lead by example — shipping code, raising the technical bar, and mentoring the engineers around you
What You’ll Do
- Set the technical direction for the team and own the architecture of AI\-powered applications from prototype through to production.
- Design, develop, and maintain high\-quality, scalable applications using modern technologies (React, Next.js, Node.js, and Python).
- Rapidly translate evolving business needs into working prototypes, then harden the successful ones into production solutions.
- Build and orchestrate AI agents and AI\-powered features using current models and frameworks.
- Apply creative problem\-solving to complex commercial and pharmaceutical challenges.
- Integrate APIs and third\-party services, and ensure performance, reliability, and maintainability across the stack.
- Lead code reviews, establish engineering best practices, and mentor and support the development of other engineers.
- Debug, troubleshoot, and optimise code to improve performance and resolve issues.
- Stay at the forefront of AI and industry trends, continuously bringing new ideas, tools, and techniques to the team.
- Collaborate with cross\-functional partners — designers, engineers, and stakeholders — to deliver well\-crafted, user\-friendly experiences.
What You Bring
- 7\+ years of professional software development experience, with a strong command of core JavaScript and TypeScript.
- Practical, production experience with React, Next.js, and Node.js, plus working proficiency in Python.
- Solid understanding of software engineering principles, architecture patterns, and best practices.
- Experience with version control systems (Git) and common Git workflows.
- A track record of rapid prototyping and shipping applications to production.
- Hands\-on experience using AI coding tools such as Cursor, GitHub Copilot, or Claude Code.
- Familiarity with prompt engineering concepts and agent\-based frameworks.
- Familiarity with AI platforms and services (OpenAI, Anthropic, and open\-source models).
- Experience mentoring engineers, leading code reviews, and setting technical standards.
- Strong understanding of both the business and technical aspects of application development.
- Strong communication skills and the ability to work effectively in a remote, collaborative, fast\-paced environment.
- Proven experience building effective AI agents using industry\-standard frameworks (Google Agents SDK, Claude Agents SDK, LangGraph, and LangChain).
- Experience assessing and implementing modern AI concepts around memory and context management, AI observability, and QA / evaluations.
- Experience with Next.js, Supabase, n8n, and AI\-powered IDEs (for example, Cursor).
- Examples of real applications built with AI, with GitHub repositories as references.
- Strong attention to detail and a passion for innovation.
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:
#### 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Newpage Digital Healthcare solutions AI Hiring
Newpage Digital Healthcare solutions has 2 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 $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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