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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Solution Engineer - **Company:** NewPage Digital Healthcare Solutions - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Airflow, Amazon Web Services, Audit Trail, Cloud Engineering, Continuous Integration, Cursor (Graphical User Interface Elements), Elasticsearch, Github, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Role-Based Access Control, Next.js, Software Engineering, Apache Solr, Data Streaming, TypeScript, Data Logging, Test-Driven Development (TDD), Data Ingestion, GitHub Copilot, Large Language Models, Apache Spark, Fastapi, Containerization, Kubernetes, Information Technology, Low Latency, Cloudflare, Machine Learning Operations, Domain Driven Design, GPT, Serverless Computing, Docker, Natural Language Generation, Jenkins, Static Application Security Testing, Microservices, Dynamic Application Security Testing - **Published:** June 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=f647259c84954990 ## About the Role Do you have experience in System deployment?, Do you have a Master's degree?, * 8+ 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. ## Description * Sit 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 & Build * Own 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 & Operate * Spin 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. ## Related Videos - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)