Senior AI & Full-Stack Engineer - Remote Impactful

Senior AI / Full-Stack Engineer

Element
14 is hiring a Senior AI / Full-Stack Engineer to build the application layer of our financial analytics work for a public sector organization focused on housing and community development. This is the seat where data science prototypes become production systems, where unstructured documents become structured signals through LLMs, and where analytical work gets into the hands of the people who use it.

You will write code across the stack — APIs, services, frontends, model integrations — and you will own outcomes, not tickets.

Element
14 is building a production financial analytics capability for a public sector organization focused on housing and community development. The organization manages a large grant and program portfolio, and the team's job is to help leadership and program offices understand it — where the money is going, how programs are performing, where the patterns in the data warrant attention, and what the modeling work can do to support better decisions.

The team produces three flavors of analytics, each with its own users and its own data. Portfolio analytics gives leadership the macro view of the program landscape. Entity analytics gives program officers and analysts the per-firm view that supports program management decisions. Transaction analytics gives operating staff a per-payment view at the point of action. We start producing real value on day one, using the organization's own data and public sources.

The work is data science and engineering applied to financial data. Building statistical and ML models on disbursement and recipient data. Designing analytical products that program staff and leadership actually use. Connecting the organization's internal systems to public datasets in ways that reveal patterns the organization could not see on its own. Careful, defensible work that holds up under scrutiny.

We are AI-first by default. Modern data science and ML are part of the toolkit on every engagement — LLMs for parsing unstructured documents and free-text fields, ML for scoring and classification, agentic workflows for repetitive analytical work. We are not chasing AI for its own sake, but we are not doing 2018-era data science either. We expect the people on this team to be fluent in current tools and to use them to be faster and sharper than the consulting median.

The data sources span the organization's own systems, commercial data, and public records. Organizational systems include the general ledger, grant tracking, and program disbursement systems. Commercial sources include major entity and identity data providers. Public sources include USA Spending.gov, FFATA sub-awards, SAM.gov, and IRS Form 990s. The interesting analytical questions almost always live at the intersection.

Beyond this engagement, we expect this team to grow with the firm. As Element
14 wins additional federal and state work, the people we hire now will help shape future engagements and the capabilities we build.

• Take data science prototypes from notebook to production. Build the scoring jobs, services, and APIs that make a model from the data scientists run reliably in a federal cloud environment. Own the notebook-to-production paved road for the team.
• Build LLM-enabled applications end-to-end. Document parsing and structured extraction from invoices, contracts, and program narratives. Agentic workflows for repetitive analytical tasks. Retrieval-augmented systems for working with the organization's program documentation. Bedrock, Azure OpenAI, or comparable FedRAMP-aligned LLM access — and the prompt engineering, evaluation, and guardrails to make them useful in a public sector context.
• Build the analytical surface that program staff and leadership actually use. Role-based views for analytics teams, program offices, and decision-makers. Dashboards where they earn their keep, application screens where the work actually happens.
• Integrate commercial data sources through API and SFTP feeds. Wire major entity, identity, and corporate-registry providers into the lakehouse and the application layer. Make them queryable and…