Applied AI Engineer

Staffworx; New York (NY); Hybrid; USD 207,000 - 253,000; Full time

Job summary

Staffworx in New York City is seeking a Forward Deployed Insights Engineer (Applied AI) for Healthcare Finance to build AI agents and predictive models that power real-world healthcare finance workflows.

You will own end-to-end delivery, work hands-on with Python and LLMs, and collaborate with customer finance and operations leaders to turn insights into decisions. On-site with some remote, visa sponsorship not provided.

Qualifications

• 3–10 years in data science, applied ML, or analytics.
• Shipping applied ML or AI agents to production end-to-end.
• Healthcare data experience preferred.
• Hands-on LLMs and agentic tooling.
• Strong Python and SQL.
• Bachelor's degree or equivalent applied ML work.

Responsibilities

• Build and configure tailored AI agents and predictive models for specific customers, on infrastructure a dedicated platform team maintains
• Own the customer context layer those agents operate on
• Validate outputs using harnesses and evals so agents are trustworthy enough to act on
• Work directly with customer finance and ops leaders to turn insights into decisions

Skills

Python

SQL

Data science

Applied ML

LLMs

Agents

Evaluations

Education

Bachelor's degree

Tools

Snowflake

Databricks

Spark

Job description

Forward Deployed Insights Engineer (Applied AI) | Healthcare Finance

Location: New York City · On-site (4 days in-office, 1 remote)

Employment type: Full-time

Salary: to $230,000 + bonus + equity

We're hiring for a venture-backed healthcare finance AI company building the agentic platform that lets US health systems manage complex financial data and workflows on demand instead of days behind.

$34M raised from top-tier US venture investors. Already live at organisations managing $5B+ in combined revenue. 30people, real traction, early equity.

What you'll do

• Build and configure tailored AI agents and predictive models for specific customers, on infrastructure a dedicated platform team maintains
• Own the customer context layer those agents operate on
• Validate outputs using harnesses and evals so agents are trustworthy enough to act on
• Work directly with customer finance and ops leaders to turn insights into decisions

Roughly 80% hands-on-keyboard, 20% client-facing. This is applied data science, Python, LLMs, agents, evals, predictive models, not platform engineering and not dashboards.

What you'll need

• 3-10 years in data science, applied ML, or analytics
• A track record of shipping applied ML, predictive models, or AI agents to production, owned end to end
• Hands-on healthcare experience: data, workflows, or products
• Hands-on LLM and agentic tooling, built and evaluated in real work
• Strong Python and SQL
• A bachelor's degree in a quantitative field, or equivalent applied ML work

Good to have: customer-facing technical experience (forward deployed, implementation), Snowflake/Databricks/Spark, CI/CD or git workflow ownership.

Not a fit if: you have no healthcare finance exposure, a purely clinical background with no finance or claims side, or need visa sponsorship. This role is US citizens and Green Card holders only, no new sponsorship available.

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