Forward Deployed Engineer

Role Overview

We are seeking a Senior Forward Deployed Engineer to sit at the intersection of engineering, product, marketing, and the business. This is not a traditional engineering role. You will work directly with stakeholders to uncover complex business problems, architect practical AI-powered solutions, and ship production systems that deliver measurable impact.

The work is highly autonomous. You will not be handed detailed specifications. You are expected to interpret product requirements, ask the right questions of the right people, and determine what should be built. The ideal candidate is a strong technical builder and architect with excellent business and product judgment, who is genuinely comfortable working alongside non-technical teams, particularly consumer marketing.

This position supports a healthcare organization, so US residency is required and experience in regulated environments is valued.

Key Responsibilities

• Design, build, and deploy AI-powered applications, agents, and automation systems using Python, FastAPI, TypeScript, Node.js, Claude, OpenAI, Gemini, and other LLM platforms.
• Work directly with marketing, product, and business teams to understand complex workflows, identify opportunities, and translate business requirements into technical solutions.
• Interpret product requirement documents and ambiguous business input independently, then define and drive the technical approach.
• Own architecture decisions across systems, data, and AI components, balancing speed of delivery with long-term maintainability.
• Build AI agents, RAG systems, tool-calling workflows, and LLM-powered applications using technologies such as LangChain, LangGraph, vector databases, embeddings, and MCP.
• Develop backend services, APIs, webhooks, integrations, and data pipelines connecting internal and third-party systems.
• Build and optimize data workflows using Snowflake, PostgreSQL, SQL, Supabase, Redis, dbt, and ETL/ELT pipelines.
• Integrate AI solutions with CRMs, marketing platforms, customer data platforms, analytics tools, and business applications.
• Build workflow automation using platforms such as n8n, Make, Zapier, and Power Automate.
• Develop internal tools, dashboards, and user-facing applications using React, Next.js, TypeScript, and Tailwind CSS when required.
• Deploy and maintain production systems using AWS or GCP, Docker, serverless infrastructure, CI/CD, and GitHub Actions.
• Prototype rapidly, gather feedback from business users, and iterate toward production-ready implementations.
• Work closely with consumer marketing teams to improve customer acquisition, engagement, retention, personalization, and operational efficiency.
• Analyze customer and business data to identify trends, bottlenecks, and opportunities for AI and automation.
• Monitor production AI systems, evaluate LLM performance, troubleshoot failures, and improve reliability and cost efficiency.
• Communicate technical solutions, limitations, and tradeoffs clearly to non-technical stakeholders.
• Own projects end to end, from discovery and architecture through development, deployment, adoption, and ongoing optimization.

Required Qualifications

• Strong professional experience with Python or a comparable backend language, along with working frontend capability.
• Proficient with Claude Code as a primary development workflow, with the judgment to know when AI output needs human review before it ships. Anthropic Claude certification (CCDV-F or CCAR-P) is a plus.
• Demonstrated experience building and deploying AI-powered applications, agents, or automation into production.
• Strong hands-on experience with LLM APIs, with Claude as the primary platform. Working proficiency across multiple models is expected.
• Software architecture experience, including designing systems rather than only implementing against someone else's design.
• Strong understanding of REST APIs, webhooks, databases, SQL, and cloud environments.
• Experience with production software development, Git, Docker, and CI/CD.
• Direct experience partnering with consumer marketing teams, with a working understanding of acquisition, engagement, retention, and personalization, and the ability to translate marketing objectives into technical solutions.
• Excellent communication skills and genuine confidence working with non-technical departments.
• Proven ability to take an ambiguous business problem and turn it into a working technical solution with minimal direction.
• Comfort operating across disciplines, including product, delivery, and quality, rather than staying strictly within an engineering lane.

Preferred Experience

• Healthcare or other regulated-industry experience.
• Experience with Claude Code, MCP, LangGraph, LangChain, RAG, embeddings, vector databases, and AI agent architectures.
• Experience with Snowflake, PostgreSQL, Supabase, Redis, dbt, and modern data pipelines.
• Experience with AWS or GCP, Docker, Kubernetes, serverless architecture, GitHub Actions, and Terraform.
• Experience with React, Next.js, TypeScript, and Tailwind CSS.
• Experience with n8n, Make, Zapier, Power Automate, or other workflow automation platforms.
• Experience integrating HubSpot, Salesforce, GoHighLevel, Segment, Customer.io, Braze, or similar CRM and marketing platforms.
• Experience with Google Analytics, Meta Ads APIs, Google Ads APIs, or other marketing and customer data APIs.
• Experience with customer data platforms, marketing automation, personalization, or consumer analytics.
• Experience building AI solutions for consumer marketing, growth, sales, customer support, or operations.
• Experience with LLM evaluation, observability, prompt optimization, and AI system cost management.
• Experience in a forward-deployed, consulting, client-facing, startup, or highly autonomous engineering environment.

Ideal Candidate

The ideal candidate is a technical problem solver with strong business instincts and the personality to match. They move comfortably between engineering, AI, data, product, and marketing, and they communicate effectively with technical and non-technical teams alike.

You should be able to carry a problem through the full arc:

Business Problem → Discovery → AI/Technical Solution → Prototype → Production → User Adoption → Optimization

The ability to build quickly, understand the business impact of what you are building, and work directly with stakeholders matters as much as technical depth.