Staff AI Solutions Engineer

This role owns the AI technical vision across the FanDuel portfolio, spanning a governed agent layer, a strategy-optimization engine, and a simulation environment. The Staff AI Solutions Engineer makes the architectural calls that set the team's direction, writes the design docs and decision records that make those calls legible to others, and acts as the technical voice of the product that other teams trust. The position leads an AI-driven team and mentors engineers across US and Romania pods, working with product, partner engineering organisations, vendors, and external delivery partners.

• Hold and evolve the technical vision across the governed agent layer, the strategy-optimization engine, and the simulation environment, making architectural calls and writing the design docs and decision records that make those calls legible.
• Serve as the technical voice of the product for other teams, translating tradeoffs into decisions they can act on and pushing back with evidence when a direction is wrong.
• Drive the integration of the agent layer into the data engineering SDLC, routing requests from Slack, Jira, GitHub and PagerDuty into a planning phase that produces scoped, reviewable tickets and a build phase that carries each ticket through spec, plan, build, test, PR and documentation.
• Build the environment that makes agentic engineering work: skills, specs, context scaffolding, shared memory, and progressive disclosure.
• Extend governed, audited data access so agents operate under each user's own permissions against the data platform, with a limited blast radius by design.
• Define the outer loop of continual learning, post-run review, and agent self-improvement that lands as reviewable pull requests rather than uncontrolled drift.
• Advance the strategy-optimization engine from proof of concept to a capability product teams request by name, landing it against real business metrics.
• Partner with product to turn ambiguous business problems into optimizable ones, defining the action space, the objective and utility weights that encode tradeoffs, and the guardrails and risk tolerance that keep the search honest.
• Own the deterministic scoring backbone: versioned scorers, shared and immutable experiment definitions, and blind holdout discipline, ensuring results are comparable, auditable and cumulative across the team rather than trapped in one person's working directory.
• Expand the engine to new scoring and optimization domains across the business, including generosity and promotional spend, module placement and ranking, and search relevance.
• Bias toward interpretable strategies that product partners can reason about, argue with, and implement without an external serving dependency.
• Mature the offline simulation environment so product hypotheses can be pre-screened against synthetic personas built from real behavioural data before committing scarce live A/B capacity.
• Establish directional agreement between simulated and live outcomes as the credibility bar, and be honest with partners about where the simulation is and is not trustworthy.
• Drive adoption of simulation as a standard input to experiment prioritization with product partners.
• Act as the team's technical lead, taking strategic direction and decomposing it into specs, skills and tickets that individual engineers can own end to end without waiting on the lead.

• 8+ years of software, machine learning, or AI engineering experience, including technical leadership of a system other engineers built on.
• A track record of turning ambiguous business problems into measurable ones, defining objectives, metrics and guardrails alongside product partners rather than waiting to be handed a spec.
• Demonstrated breadth: credible in a technical design review, in a product prioritization conversation, and in front of senior stakeholders.
• Proven ability to decompose a vague strategic direction into workstreams that individual engineers can own autonomously.
• Expert-level Python in production settings: typing, testing, packaging, and performance awareness.
• Hands-on experience using agentic coding harnesses as real engineering tools, and designing the skills, context, tool access and memory that make them effective.
• Production LLM application experience: agent orchestration, tool and function calling, structured output handling, and retrieval.
• Strong optimization and experimentation background: search over strategy spaces, deterministic scoring, offline/online metric alignment, holdout discipline, and A/B design and interpretation.
• Cloud-native engineering on AWS, plus hands-on work in a modern data platform such as Databricks.
• Bachelor's degree in Computer Science, a related technical field, or equivalent industry experience.

• Knowledge of sports and/or experience working within a heavily regulated industry.

• An array of health plans to choose from, some as low as $0 per paycheck.
• Programmes for fertility and family planning.
• Mental health support.
• Fitness benefits.
• Generous paid time off (PTO and sick leave).
• Annual bonus and long-term incentive opportunities, based on performance.
• 401k with up to a 5% match.
• Commuter benefits.
• Pet insurance.
• Medical, vision and dental insurance.
• Life insurance and disability insurance.
• Short-term or long-term incentive compensation, including cash bonuses and stock program participation.

FanDuel Group is the premier mobile gaming company in the United States and Canada, with a portfolio of leading brands across mobile wagering including America's #1 Sportsbook, FanDuel Sportsbook; its leading iGaming platform, FanDuel Casino; FanDuel Racing, the industry's leader in horse racing and advance-deposit wagering; and its daily fantasy sports product. FanDuel Group is a subsidiary of Flutter Entertainment, the world's largest sports betting and gaming operator, traded on the New York Stock Exchange (NYSE: FLUT).