Senior Forward Deployed Engineer - Wellness

Advatix, Inc.•,

1d•$170,000 - $240,000•Remote

About The Position

HRforGrowth® is engaged as the talent acquisition partner to conduct this search on behalf of a client organization that is hiring for this role. HRforGrowth is not the employer for this position. HRforGrowth identifies, evaluates, and presents top-tier candidates through its global talent acquisition practice; all employment decisions — including final selection, offer terms, compensation, and conditions of employment — are made solely by the hiring organization. Our Client is seeking a highly experienced and entrepreneurial Senior Forward Deployed Engineer to own the full technical delivery lifecycle for wellness clients — from pre-sales demonstrations through post-sale integration and production deployment. This role is ideal for an engineer-architect hybrid who can build AI-powered systems end-to-end while working directly with client technical teams and helping shape the core product through real-world customer requirements. The successful candidate will work with digital and location-based fitness providers to build production AI systems, integrate complex client data sources, and translate customer workflows into AI-orchestrated solutions. The ideal candidate will combine strong production engineering and software architecture experience with polished client-facing communication and an entrepreneurial, startup-minded approach.

Requirements

Minimum 5 to 15 years of professional software engineering experience.
Experience owning full technical delivery for clients, including pre-sales, scoping, and post-sale integration.
Proven experience building and deploying customer-facing software systems end-to-end, through production engineering, solutions engineering, solutions architecture, or technical consulting.
Ability to design end-to-end system architectures and produce complete architecture diagrams.
Strong software architecture skills, including the ability to make integration decisions and articulate technical tradeoffs.
Experience building data and machine learning pipelines, including PySpark, ETL/ELT, unstructured data, document ingestion, and predictive models.
Fluency in modern programming languages and frameworks, including JavaScript/Node.js, TypeScript, Python, Next.js, and React.
Strong client-facing communication and professional presence.
Ability to run technical working sessions and present technical solutions to clients.
Production engineering experience shipping complex, customer-facing software systems.
Strong ability to work independently and make autonomous technical decisions.

Nice To Haves

Experience as a founding engineer, startup co-founder, startup CTO, or software engineer at a high-growth startup.
Experience in formal machine learning or internal data science work on healthcare-related projects.
Experience building LLM systems or AI agents, including: Prompt engineering, Function calling, Structured output, Guardrails, Observability.

Responsibilities

Own the full technical delivery lifecycle for wellness clients, including pre-sales demonstrations, requirements gathering, design sessions, implementation, testing, and production deployment.
Build customer-facing demonstrations and translate client requirements into production-ready technical solutions.
Architect and build AI agents and insight products using platform tools such as feature stores, knowledge bases, MCP, and agentic context packages.
Integrate client data systems, including CRMs and wellness applications.
Design and implement data ingestion processes, ETL pipelines, and solutions for heterogeneous data sources.
Collaborate directly with clients' data and technical teams through virtual technical working sessions.
Translate client workflows and requirements into production AI-orchestrated solutions.
Design complete system architectures and make technical integration decisions based on client requirements.
Articulate technical tradeoffs and develop architecture diagrams, API designs, and fallback strategies.
Build and deploy complex, customer-facing software systems in production.
Make autonomous technical decisions while operating across multiple aspects of client engagements.
Feed learnings from client deployments back into the core platform, spending approximately 10–20% of time influencing product direction through real-world client patterns.
Collaborate with internal product and engineering teams to incorporate customer learnings into reusable platform capabilities.
Work across engineering, architecture, data, AI, and client-facing responsibilities as required by each engagement.