Senior AI / Full-Stack Engineer

Element14 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.

What you would work on

Element14 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 oversight and program management. 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 USASpending.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 Element14 wins additional federal and state work, the people we hire now will help shape future engagements and the capabilities we build.

What you will do

• 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 trustworthy.
• Operate across the stack. Backend services in Python or Node.js. Frontend in React or comparable. Cloud infrastructure as code (Terraform, CloudFormation, or CDK). CI/CD that you take seriously. You do not need to be expert in all of it, but you need to be effective in most of it and willing to fill in the rest.
• Use modern AI coding tools deliberately. Claude, Cursor, Copilot, agentic coding workflows — we expect daily use, opinions about where they help and where they do not, and the judgment to use them as a force multiplier rather than a substitute for understanding.

Who we are looking for

In addition to the qualities we look for in everyone on the team:

• Genuine interest in public service. You are excited about helping government agencies operate more effectively and advance their mission. The variety appeals to you — one quarter you might be working on financial integrity for housing programs, the next on agricultural data, the next on health programs.
• Commitment to technical craft. You stay current. You can point to tools and techniques you have picked up in the last twelve months and explain why they matter. Cloud, data, and AI are central to what we do, and we expect that to be central to how you think too.
• Strong communication and relationship-building. You build trust with government stakeholders by listening carefully, explaining clearly, and following through. You learn the program — not just the data — deeply enough that clients want you in the room.
• Drive and ownership. This is not a clock-in, clock-out role. We want people who care, and that shows up in their work — anticipating the next question, fixing what is broken before being asked, and treating the mission as their own.
• Strong full-stack engineering. Five or more years building production systems, with real range across the stack. Strong Python and at least one of TypeScript or Node.js. Comfortable building REST or GraphQL APIs, async background jobs, and modern frontends in React or comparable. You write code other engineers can read and maintain, and you have shipped systems that real users depended on.
• AWS depth required. Hands-on experience with AWS services that production federal workloads actually run on — some combination of Lambda, ECS or EKS, S3, RDS, API Gateway, Step Functions, Cognito or comparable. You can architect a service end-to-end, reason about IAM and VPC isolation, and ship to a FedRAMP-aligned environment without surprise. Familiarity with GovCloud is a plus.
• LLM application experience. You have shipped at least one production system that uses LLMs for something real — document extraction, retrieval-augmented generation, agentic workflows, or comparable. You understand the difference between a demo and a production LLM system. You have opinions about evaluation, guardrails, prompt versioning, and cost.
• Infrastructure as code. You have provisioned and maintained infrastructure with Terraform, CDK, CloudFormation, or comparable. You write IaC the way you write application code — version-controlled, reviewable, and reproducible. You do not click through consoles and call it done.
• Applied disposition. You like real problems and shipping real systems. You roll up your sleeves. You would rather solve a hard problem that matters than build the perfectly architected system that ships in eighteen months. You take responsibility for outcomes.
• AI-first as a working style. You use modern AI coding tools as a daily part of your workflow and have a clear point of view about where they accelerate engineering and where they get in the way.
• Preferred: evidence of work you have done outside a paid job — open-source contributions, side projects, academic research, hackathons, or a portfolio you can walk us through. We read these as signals of curiosity and craft.
• Also a plus: experience with FedRAMP environments or AWS GovCloud; experience integrating major commercial data vendors; experience building data products or workflow tools for analytics teams, program staff, or decision-makers; experience with Databricks, Snowflake, or comparable lakehouse environments; experience with graph databases or entity-resolution tooling.
• Required: ability to obtain a U.S. Federal Public Trust clearance. This requires U.S. citizenship or lawful permanent residency and a successful background investigation.

Salary range: $160,000 – $200,000 annual base. Final offer determined based on experience, depth, and the specific seat. We post ranges because we believe in transparency about pay.

Location: Hybrid — Washington, DC metro area preferred for periodic on-site collaboration with the client. Remote candidates within the United States considered for the right fit. U.S. work authorization required.

Perks & benefits

• Health, dental & vision coverage. Comprehensive medical plans with generous company support toward your premiums.
• 401(k) retirement plan. Save for the future, pre-tax.
• Remote-friendly across the U.S. Work from anywhere in the United States, with periodic on-site collaboration in the DC metro area where the work calls for it. We value autonomy and trust.
• Mission-driven work. Meaningful projects that change how government uses data and technology in service of the public.
• Small, tight-knit team. A senior bench where your ideas matter and your growth is encouraged.

Element14 is an equal opportunity employer. We are committed to building a team that reflects the public we serve.