Solution Engineer

AI SOLUTION ENGINEERLocation: Remote | Type: ContractAbout Newpage SolutionsNewpage Solutions is a global digital health innovation company helping people live longer, healthier lives. We partner with life sciences organizations—including 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 Great Place to Work® certified for three consecutive years, hold a top Glassdoor rating, and were named among the "Top 50 Most Promising Healthcare Solution Providers" by CIO Review.As a remote-first company, we foster creativity, continuous learning, and inclusivity, creating an environment where bold ideas thrive and make a measurable difference in people’s lives.Newpage looks for candidates who are invested in long-term impact. Applications with a pattern of frequent job changes may not align with the values we prioritize.Your MissionWe are hiring Solution Engineers to lead the technical shape of our most ambitious AI builds. You will sit at the front of the room when scope is still forming, and you will be in the codebase the same afternoon.This is an engineering partner role for senior builders who own architecture-level decisions on LLM-powered systems and carry trade-off conversations with business and clinical leaders without locking them in. You will decide what is worth hardening, what should be discarded after a week, and where the riskiest assumption sits on every loop.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 with Claude Code, Cursor, agents, eval harnesses, MCP, and modern TypeScript and Python.What You’ll DoShape the PlatformSit with business, product, and clinical leaders to reframe ambiguous problems into something concrete, scoped, and buildable.Validate the riskiest assumption first on every new loop—prototype, react, decide what survives.Carry architecture and trade-off conversations with stakeholders directly. Demo live without a slide deck.Lead POCs, innovation sprints, and research experiments to validate emerging AI techniques before they get baked into platform decisions.Author ADRs and scope memos for major decisions—LLM abstractions, retrieval design, vendor selection, integration boundaries, infrastructure path.Architect & BuildOwn end-to-end architecture for AI-powered platforms: retrieval, reasoning, evaluation, integration, and the seams between them.Design vendor-agnostic LLM abstractions so frontier models (Claude, GPT, Gemini, open-weight) can be swapped behind a clean interface as enterprise constraints evolve.Architect and ship production-grade agentic systems using LangGraph, AutoGen, Claude Agent SDK, OpenAI Assistants, or your own orchestration layer.Build modular backends in Python or TypeScript aligned with clean architecture, OOP, SOLID, and domain-driven design.Apply RAG techniques where they actually help: vector databases (Pinecone, Chroma, Weaviate, pgvector), hybrid retrieval with ElasticSearch or Solr, BM25 + similarity, re-ranking.Design prompt and context engineering frameworks that optimize accuracy, repeatability, cost, and latency.Use AI-assisted development tools (Claude Code, Cursor, GitHub Copilot, Codex) through structured workflows, sub-agents, skills, and templates—with discipline and review.Productionize & OperateSpin 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) or serverless (Lambda)—chosen for fit, not preference.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, audit trails, RBAC, structured logging, metrics, tracing, automated CI/CD (GitHub Actions, Jenkins).Engage enterprise architecture review paths early when they apply. Move with them, not around them.Mentor others on system design, agentic patterns, and AI engineering best practices.What You Bring8+ years engineering experience, with architecture-level ownership on at least one production system.Direct hands-on experience designing and shipping LLM-powered products end-to-end: RAG pipelines, prompt and context engineering, eval harnesses, vendor-agnostic LLM abstractions.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 or TypeScript, with OOP, SOLID, 12-factor application development, and microservice architecture. You have built Next.js applications, FastAPI services, and similar.End-to-end implementation experience with vector databases, retrieval pipelines, and eval harnesses.Cloud-native AWS deployment experience—with Docker, Kubernetes, and GitHub Actions. 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.A deep working understanding of how LLMs behave—and where they break—and how to optimize for accuracy, latency, and cost.Track record working with evolving requirements and co-creation models, not finished specs.Strong written communication—you author ADRs, scope memos, and decision documents that hold up under review.Comfortable making trade-off calls in front of business leadership without locking them in.A real, recent trail of built things: GitHub, a portfolio, side projects, indie tools, or OSS contributions.A no-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost.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.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).Experience with enterprise AI governance frameworks (EU AI Act readiness, internal AI policy / risk frameworks).Healthcare or life sciences domain exposure.Pharma, healthcare, or other regulated-industry experience.Relevant cloud architecture certifications.What We OfferAt 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:Flexible, remote-first work – Choose where you work best while staying connected to a global, collaborative team.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 recognizes both contribution and potential.Ready to Apply?Let’s build the future of health together. Apply below or reach out to Shilpa.shetty@newpage.io with any questions.