AI Forward Deployed Engineer - CRM & Marketing

Hard Rock Digital•United States, FL

1d•Hybrid

About The Position

Hard Rock Digital is building a team focused on becoming the best online sportsbook, casino, and social gaming company in the world. We’re looking for an AI Forward Deployed Engineer to build agentic and generative AI for CRM and Marketing. This role will ship production features that help acquire, engage, and retain millions of players across sportsbook, casino, and social casino. The engineer will work hands-on with agents, prompt engineering, and RAG over customer and product data, with a strong emphasis on responsible gaming and compliance due to the regulated nature of the industry.

Requirements

4+ years in software or AI engineering, or a relevant degree.
Strong Python, with experience building and operating production services (REST APIs, microservices, containers).
Hands-on experience integrating LLMs (Anthropic Claude, OpenAI GPT, Amazon Bedrock) and strong prompt and system-prompt engineering.
Familiarity with RAG and experience with vector databases (for example Pinecone, Faiss, or pgvector).
Strong SQL and experience with a modern cloud data warehouse, Snowflake preferred.
Hands-on experience with a CRM or marketing-automation platform, Braze strongly preferred.
Working understanding of customer lifecycle, segmentation, personalization, and LTV.
Experience with a cloud platform (AWS, GCP, or Azure) and its AI and ML services.

Nice To Haves

Agentic frameworks and automation such as MCP, ADK, or n8n.
dbt and analytics-engineering practices, including data-quality testing.
Frontend experience with React or Next.js (this role can flex full-stack).
Kubernetes and Helm, plus OAuth2 and enterprise identity (Entra) for service auth.
Recommendation systems, marketing attribution, or generative content at scale.

Responsibilities

Build and ship agentic and generative-AI features for CRM and marketing, from the Python and FastAPI backend to the Next.js frontend.
Power personalization and decisioning, including next-best-offer, recommendations, and audience and value-based segmentation from behavioral and LTV signals.
Generate and optimize lifecycle and marketing content with LLMs, keeping it on-brand, localized, and compliant with responsible-gaming rules.
Model customer lifecycle states and journeys, and surface the growth levers that move retention and LTV.
Integrate deeply with Braze, including Canvas and Journey orchestration and syncing lifecycle state onto user profiles.
Build the data and retrieval foundation: RAG, vector search, and MCP servers over our Snowflake and dbt marts.
Measure impact through attribution, ROAS, and treated-versus-holdout experiments on acquisition, retention, and LTV.
Ship reliably in production and keep pace with advances in AI and martech.