Staff AI Solutions Engineer

FanDuel • Atlanta, GA

2d • $159,000 - $208,950

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 to product and engineering partners by translating tradeoffs into actionable decisions and challenging directions with evidence.

You will drive agentic engineering across the data engineering software development lifecycle, build the supporting environment and governance, and establish continual learning and review processes. You will advance the strategy-optimization engine into a product capability grounded in business metrics, and mature offline simulations to help product teams assess hypotheses before using live A/B testing capacity.

As the team’s technical lead, you will turn strategic direction into specifications, skills, and tickets that engineers can own independently; set the working model for human- and agent-led engineering; mentor engineers across US and Romania pods; and represent the team 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 built on by other engineers.
• Track record of turning ambiguous business problems into measurable objectives, metrics, and guardrails in partnership with product.
• Ability to contribute credibly to technical design reviews, product prioritization conversations, and discussions with senior stakeholders.
• Ability to decompose vague strategic direction into workstreams engineers can own autonomously.
• Expert-level Python experience in production settings, including typing, testing, packaging, and performance awareness.
• Hands-on experience using agentic coding harnesses as engineering tools and designing the skills, context, tool access, and memory that make them effective.
• Production LLM application experience, including agent orchestration, tool and function calling, structured output handling, and retrieval.
• Strong optimization and experimentation background, including strategy-space search, deterministic scoring, offline/online metric alignment, holdout discipline, and A/B test design and interpretation.
• Cloud-native engineering experience on AWS and hands-on experience 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

• Own and evolve the technical vision for the governed agent layer, strategy-optimization engine, and simulation environment.
• Make architectural decisions and document them in design documents and decision records.
• Translate technical tradeoffs into actionable decisions for product and engineering partners, and challenge incorrect directions with evidence.
• Integrate the agent layer into the data engineering SDLC, routing requests from Slack, Jira, GitHub, and PagerDuty through planning and build workflows.
• Build agentic engineering capabilities, including skills, specifications, context scaffolding, shared memory, and progressive disclosure.
• Extend governed, audited data access so agents use each user's permissions and have 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 to define action spaces, objectives, utility weights, guardrails, and risk tolerance for optimization problems.
• Own deterministic scoring practices, including versioned scorers, shared immutable experiment definitions, and blind holdout discipline.
• Expand optimization into business areas including promotional spend, module placement and ranking, and search relevance.
• Favor interpretable strategies product partners can understand and implement without an external serving dependency.
• Mature offline simulations using synthetic personas based on real behavioral data to pre-screen product hypotheses.
• Establish directional agreement between simulated and live outcomes as a credibility bar and communicate simulation limitations.
• Drive adoption of simulation as an input to experiment prioritization with product partners.
• Decompose strategic direction into specifications, skills, and tickets that engineers can own end to end.
• Set the team's AI-driven working model, including what humans specify, what agents build, and what humans review.
• Raise engineering standards through code review, reusable patterns, and design review.
• Mentor engineers across US and Romania pods and communicate clearly across asynchronous collaboration.
• Represent the team technically with partner engineering organizations, vendors, and external delivery partners.

Benefits

• Health plans, including some options with no cost per paycheck.
• Programs for fertility and family planning.
• 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.
• Paid personal time off.
• 14 paid company holidays.
• Paid sick time in accordance with applicable state and federal laws.
• Potential short-term or long-term incentive compensation, including cash bonuses and stock program participation. Benefits vary by location, role, and level.

What This Job Offers

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