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