Newpage Solutions
Website:
newpage.io
Job details:
Job Description
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 serverlesschosen 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.
Mentor others on system design, agentic patterns, and AI engineering best practices.
Required
What You Bring :
- 3+ years relevant experience 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 no-compromise attitude on clean code, TDD, security, observability, scalability, performance, and cost.
- A deep working understanding of how LLMs behave-and where they break-and how to optimize accuracy, latency, 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 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).
- Healthcare or life sciences domain exposure.
- AWS Professional certification or other relevant industry certifications
(ref:hirist.tech)
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