Full stack Software Engineer (AI Enablement Engineer)

Full Stack Software Engineer AI Enablement Engineer

Location: Rockville, MD / McLean, VA (Hybrid)

Pay Rate: . ./hour on W2

Employment type: W2

Eligibility: Candidates must be local to the DC/VA/MD area and available for an assessment and onsite interview.

Job Description:

This position supports a newly approved AI Finance Readiness initiative focused on improving finance and administrative business processes through AI-enabled discovery, proof-of-concepts, and targeted implementations.

The engineer will partner with business stakeholders, architects, and technical teams to evaluate opportunities across systems such as Workday Financials, FlexTrack, and other finance-related platforms.

The goal is not to build foundational AI models, but rather to determine where modern AI technologies can improve workflows, efficiency, analytics, and decision-making.

Key Focus Areas

AI-Driven Business Process Discovery

Evaluate finance and administrative workflows to identify opportunities for AI-enabled improvements.

Conduct discovery sessions with business stakeholders and help assess feasibility of potential AI solutions.

Support POCs and pilot initiatives that may ultimately lead to production implementations.

Analyze existing processes and determine whether AI enhancements or alternative workflows provide the greatest value.

Technology Environment

Strong engineering foundation in:

Java

AWS

Python

Exposure to modern AI platforms and tools such as:

Claude

Amazon Bedrock

LLM-based solutions

Experience with analytics platforms, automation tools, and SaaS integrations is beneficial but not required.

Previous experience with RPA or workflow automation initiatives is highly relevant, as the organization views AI as a natural evolution of past automation efforts.

What They Are NOT Looking For

Not seeking an AI researcher or someone building foundational LLMs.

Not looking for a "unicorn" with every emerging AI skill.

Not interested in candidates who simply leverage AI-generated code without understanding architecture, tradeoffs, and implementation details.

This is not a pure data science or machine learning engineering role.

Ideal Candidate Profile

Must-Have Characteristics

Strong senior-level software engineer with solid Java, AWS, and Python experience.

Demonstrated curiosity and active investment in learning modern AI technologies.

Ability to quickly evaluate AI use cases and determine technical feasibility.

Strong communication and stakeholder engagement skills.

Comfortable translating business problems into practical technical solutions.

Able to articulate pros, cons, risks, and implementation approaches during POC discussions.

Nice-to-Have Experience

SaaS integration experience.

Workday Financials or enterprise finance systems exposure.

FlexTrack experience.

Analytics platform integration experience.

RPA or intelligent automation background.

Personality & Mindset

Strong engineering judgment and architectural thinking.

Consultative approach with business users.

Comfortable navigating environments where stakeholders have differing opinions on AI solutions.

Learning-oriented and adaptable to rapidly evolving AI technologies.

Preference for senior engineers who can operate independently, though exceptional mid-level candidates with strong learning agility will also be considered.

Interview Screening Themes

Engineering Fundamentals

Depth in Java, AWS, and Python.

Architecture and design thinking.

Ability to evaluate technical tradeoffs.

AI Adoption & Learning Mindset

How are they using Claude, Bedrock, Copilot, or similar tools?

Examples of AI-enabled solutions they have explored or implemented.

How they stay current with emerging AI technologies.

Business Partnership

Experience gathering requirements and working directly with business stakeholders.

Ability to challenge assumptions and assess feasibility.

POC & Innovation Experience

Examples of leading discovery efforts, pilots, or proof-of-concepts.

How they evaluated success criteria and implementation options.

Communication

Ability to explain technical concepts to non-technical audiences.

Experience influencing architects, business leaders, and engineering teams.