Staff AI Solutions Engineer

FanDuel • Atlanta, GA

2d

About The Position

As a Staff AI Solutions Engineer at FanDuel, you will help own the AI technical vision across the portfolio, including the governed agent layer, strategy-optimization engine, and simulation environment. You will make architectural decisions, document them through design documents and decision records, and serve as a trusted technical voice for product and engineering partners by explaining tradeoffs and challenging directions with evidence.

You will integrate agentic capabilities into the data engineering software development lifecycle, advance optimization into a product capability grounded in business metrics, and mature offline simulation so product hypotheses can be evaluated before using live A/B capacity. As the team’s technical lead, you will turn strategic direction into work engineers can own, establish an AI-driven working model, mentor engineers across US and Romania pods, and represent the team in joint work with partner engineering organizations, vendors, and external delivery partners.

Requirements

• At least 8 years of software, machine learning, or AI engineering experience, including technical leadership of a system used by other engineers.
• Track record of translating ambiguous business problems into measurable objectives, metrics, and guardrails in partnership with product teams.
• Breadth to contribute credibly to technical design reviews, product prioritization discussions, and senior stakeholder conversations.
• Ability to decompose strategic direction into workstreams that engineers can own autonomously.
• Expert-level Python in production settings, including typing, testing, packaging, and performance awareness.
• Hands-on experience using agentic coding harnesses as engineering tools and designing effective skills, context, tool access, and memory.
• Production LLM application experience, including agent orchestration, tool and function calling, structured output handling, and retrieval.
• Strong optimization and experimentation background, including search over strategy spaces, deterministic scoring, offline/online metric alignment, holdout discipline, and A/B test design and interpretation.
• Cloud-native engineering experience on AWS and hands-on work with a modern data platform such as Databricks.
• Bachelor’s degree in Computer Science, a related technical field, or equivalent industry experience.

Nice To Haves

• Knowledge of sports.
• Experience working within a heavily regulated industry.

Responsibilities

• Drive integration of the agent layer into the data engineering SDLC, from incoming requests through planning, scoped tickets, implementation, testing, pull requests, and documentation.
• Build the supporting environment for agentic engineering, including skills, specifications, context scaffolding, shared memory, and progressive disclosure.
• Extend governed and audited data access so agents use each user’s permissions and operate with a limited blast radius.
• Define continual learning, post-run review, and agent self-improvement processes that produce reviewable pull requests.
• Advance the strategy-optimization engine from proof of concept to a product capability tied to real business metrics.
• Partner with product teams to define optimization action spaces, objectives, utility weights, guardrails, and risk tolerances.
• Own deterministic scoring practices, including versioned scorers, immutable experiment definitions, and blind holdout discipline.
• Expand optimization into additional business domains, including promotional spend, module placement and ranking, and search relevance.
• Promote interpretable strategies that product partners can understand and implement without an external serving dependency.
• Mature the offline simulation environment using synthetic personas based on real behavioral data to pre-screen product hypotheses.
• Establish directional agreement between simulated and live outcomes as a credibility standard, communicate simulation limitations, and drive adoption in experiment prioritization.
• Decompose strategic direction into specifications, skills, and tickets that engineers can own end to end.
• Set the team’s AI-driven working model, defining what humans specify and review and what agents build; raise quality through code reviews, reusable patterns, and design reviews.
• Mentor engineers across US and Romania pods and communicate clearly in asynchronous collaboration.
• Represent the team technically with partner engineering organizations, vendors, and external delivery partners.

Benefits

• Health plans, including options as low as $0 per paycheck.
• Fertility and family-planning programs.
• Mental health support.
• Fitness benefits.
• Paid time off and sick leave.
• Annual bonus and long-term incentive opportunities based on performance.
• 401(k) with up to a 5% match.
• Commuter benefits.
• Pet insurance.
• Medical, vision, and dental insurance.
• Life insurance.
• Disability insurance.
• Short-term or long-term incentive compensation, potentially including cash bonuses and stock program participation.
• 14 paid company holidays.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior