Lead FDE Production & Consulting

LEAD FDE- PRODUCT AND CONSULTING

Location: Hybrid | Type: Full-time

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 a Lead Forward Deployed Engineer to hold three disciplines that are usually split across three different people: the consultant who earns the room, the product owner who decides what is worth building, and the engineer who builds it and stands it up in production.

You will sit inside our clients' organizations—pharma, biotech, and healthcare—reframe vague, high-stakes problems into something concrete, decide what deserves to exist, and ship it end to end. You won't wait for a refined backlog, a PM in the middle, or a separate platform team. You will shape the idea, argue for the direction, build the thing, and own the outcome.

This is a builder-first individual-contributor role with genuine product and commercial ownership. It suits engineers who live at the current edge of AI development—Claude Code, Cursor, agents, eval harnesses, MCP ,modern TypeScript and Python—and who are equally at home at a whiteboard in front of a client's executive team

What You’ll Do

Consult: Earn the Room and Frame the Problem

• Run discovery with business, and technical stakeholders—and separate the problem they stated from the problem they have.
• Structure ambiguity into something decidable: hypotheses, current-state and future-state maps, and a short set of options with honest trade-offs rather than a menu of everything possible.
• Build and defend the case for investment—value drivers, cost to serve, adoption risk, and what has to be true for it to pay off.
• Communicate at every altitude—a five-minute steering committee readout, a written decision memo, and a whiteboard architecture session in the same day.

Own the Product: Decide What's Worth Building

• Turn Product discovery into a lean, sequenced roadmap framed around outcomes rather than features, and re-cut it when the evidence changes.
• Write the artefacts that make delivery unambiguous: problem briefs, PRDs, user stories, acceptance criteria, and a real definition of done.
• Define success metrics up front—adoption, task completion, cycle time, model quality, cost per outcome—and instrument them into the product rather than reconstructing them later.
• Design for the user, not the demo: partner with UX, run usability sessions, watch real people use the thing,and act on what you see.
• Run the delivery cadence—backlog, sprints, demos, releases—with enough process to be predictable and not one step more.
• Own the product after launch: adoption, feedback loops, iteration, and the honest call to sunset what isn't working.

Build (fast) with AI

• When the brief is clear, head down and produce. Know what to build by the end of the conversation; have a working prototype to react to by the end of the week.
• Build modular backends in Python or TypeScript aligned with clean architecture, OOP, SOLID, and domain-driven design.
• Create fullstack applications, APIs, agents, workflows, and similar systems using frameworks such as Next.js, React, FastAPI, Fastify, FastMCP, and Hono.
• Architect and ship 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, and BM25 + similarity search.
• Work across relational, document, key-value, and graph stores as the problem demands; use event-driven patterns where they fit, not by default.
• Design prompt and context engineering frameworks that optimize accuracy, repeatability, cost, and latency.
• Use AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, Codex) through structured workflows, native instructions, templates, and sub-agents—with discipline and review.
• Fine-tune or adapt models where the problem genuinely calls for it.

Test, Deploy, Productionize

• Spin up the infra, write the evals, wire up the MCP servers, deploy the agents, and harden the bits that survive contact with real users.
• Deploy on AWS, Azure, Cloudflare, or Vercel using containerization (Docker, Kubernetes) or serverless—chosen for fit, not preference.
• Treat evals as a first-class discipline: hands-on harnesses, not theoretical frameworks. Build with a clear-eyed view of where current AI tooling helps and where it falls short.
• Apply engineering practices that hold up in production: TDD, secrets management and rotation,SAST/DAST, structured logging, metrics, tracing, and automated CI/CD (GitHub Actions, Jenkins).
• Own what you build end-to-end, including the infrastructure and operations that keep it running.

Lead and Multiply

• Mentor engineers on system design, agentic patterns, AI engineering practice, product thinking, and client craft.
• Set the technical and product standard on your engagement—review the work, raise the bar, and unblock people quickly.
• Contribute to Newpage's accelerators, playbooks, and reusable assets so the next team starts further ahead than you did.
• Interview, onboard, and grow the people who will do this work after you.

What You Bring

Engineering

• 6+ years building production software, including 3+ years building production applications using AI /agentic development approaches—fullstack applications, agents, workflows, MCPs, and more.
• 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.
• Active, structured use of AI-assisted development tools (Claude Code, Cursor, GitHub Copilot) with demonstrable workflows, sub-agents, skills, and innovative approaches.
• Strong Python or TypeScript, with OOP, SOLID, 12-factor application development, and microservice architecture. You've built Next.js applications, FastAPI services, and similar.
• End-to-end implementation experience with vector databases, retrieval pipelines, and eval harnesses.
• Cloud-native deployment experience across at least one of AWS, Azure, Cloudflare, or Vercel—with Docker, Kubernetes, and GitHub Actions.
• A deep working understanding of how LLMs behave—and where they break—and how to optimize accuracy,latency, and cost.

Product

• Demonstrable end-to-end ownership of a product or a major product surface—you decided what to build,not only how to build it.
• Fluency with product discovery and delivery practice: problem framing, opportunity sizing, roadmapping,prioritization frameworks, PRDs, acceptance criteria, and outcome metrics.
• Experience defining and instrumenting success metrics—and a specific example of changing direction because the data told you to.
• Comfort working alongside design: you can critique a flow, sketch a wireframe, and tell a genuinely good experience from a demo-able one.
• Experience running an agile delivery cadence as the person accountable for the outcome, not just a participant in the ceremony.

Consulting and Client Leadership

• Client-facing experience in a consulting, professional services, or forward-deployed capacity—you have owned the relationship, not just attended the call.
• Ability to structure genuinely ambiguous problems and land a clear recommendation with a non-technical executive audience.
• Strong written communication: decision memos, proposals, and readouts that stand on their own without you in the room to narrate them.
• Experience with scoping, estimation, change control, and expectation management across a multi-stakeholder engagement.
• The credibility to disagree with a client—with evidence, and without damaging the relationship.

Foundations

• A no-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost.
• Clear writing and a willingness to reframe problems in conversation rather than wait for someone else to
• define them.
• 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 and commercial challenges.
• Willingness to travel to client sites as engagements require.
• Bachelor's or Master's in Computer Science, Machine Learning, or a related technical discipline.

Bonus Skills/Experience

• Life sciences or healthcare domain exposure—clinical, commercial, regulatory, or R&D.
• Background from a top-tier consultancy, a product-led startup, or both.
• Public writing, talks, or threads about building with AI.
• AWS Professional certification or other relevant industry certifications.

What 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

Ready to Apply?

Let’s build the future of health together. Apply below or reach out to: