Associate Director, AI Solutions Architecture

Company descriptionSpark Foundry was built during the most transformative time in the history of advertising and marketing. We deliver everything a traditional media agency has to offer and have grown into one of the world's most successful agencies by helping brands evolve their marketing by leveraging identity, commerce, artificial intelligence, and more to connect with people. Transformation is part of our DNA. Spark Foundry's start-up spirit delivers high-touch approaches and a never-ending desire to challenge the status quo. Combined with Publicis Media's powerhouse soul leveraging connected data assets, advanced AI applications, and investment clout, we Bring HEAT to Brands. No other agency possesses the expertise that we do to address today's most pressing challenges to drive business transformation through media.
OverviewAssociate Director, ML & AI is a senior, hands-on engineering leader responsible for driving the design, development, and delivery of scalable ML & AI solutions. This role will own the technical direction of priority initiatives, establish engineering standards, and ensure high-quality execution across data engineering and AI/ML use cases. You'll build GenAI-first solutions (LLM-powered assistants, insight synthesis, workflow automation) alongside traditional ML systems (forecasting, optimization, recommendations). This is a hands-on, product-focused role for someone who ships production systems not research prototypes.
Associate Director will work closely with analytics and media teams to translate business requirements into robust technical solutions. This role is ideal for someone who enjoys building from scratch, setting direction, and unblocking teams, while remaining deeply involved in hands-on development.
ResponsibilitiesOwn the technical design and architecture for GenAI and ML solutions end to endDefine standards for LLM usage, RAG patterns, evaluation, and production readinessMake architectural tradeoffs across scalability, cost, latency, and model qualityEnsure GenAI solutions are secure, observable, and built for real-world useLead development of GenAI applications, including LLM workflows, RAG pipelines, and inference servicesBuild and optimize data pipelines and feature workflows to support GenAI and traditional ML use casesDevelop production-grade APIs and backend services using cloud-native and distributed systemsTroubleshoot complex issues across data quality, retrieval accuracy, model behavior, and system performanceDrive priority AI initiatives from concept through production deploymentBreak down ambiguous problems into clear technical tasks, milestones, and MVPsOwn delivery timelines, dependencies, and technical risk managementPartner with stakeholders to balance speed, scope, and long-term maintainabilityServe as the primary technical point of contact for GenAI and ML initiativesTranslate business workflows into scalable technical designsSupport stakeholder and client discussions requiring deep technical contextEnsure alignment between upstream data sources and downstream AI consumptionLeverage and extend existing AWS and Databricks infrastructure for GenAI workloadsIntegrate vector databases, orchestration frameworks, and ML platforms into a cohesive stackEstablish monitoring, logging, and alerting AI pipelines, cost, latency, and qualityPromote secure, compliant, and cost-aware engineering practicesMentor engineers and analysts through design reviews, code reviews, and technical guidanceSupport onboarding and skill development for GenAI and ML capabilitiesFoster a collaborative, high-ownership engineering cultureIdentify opportunities to simplify, automate, and modernize AI systemsEvaluate new GenAI tools, models, and platforms and recommend adoption when appropriateContribute to documentation, knowledge sharing, and long-term technical roadmap planningQualifications5years of experience in software engineering, data engineering, or platform developmentProven ability to lead technical initiatives end-to-endHands-on experience shipping GenAI / LLM-powered applicationsStrong understanding of RAG architectures, prompt engineering, and orchestration frameworksExperience with vector databases for semantic searchAbility to build functional frontends (Streamlit, Gradio, React, etc.)Strong experience designing and building scalable data and analytics systemsAdvanced proficiency in SQL and strong experience with Python or similar languagesHands-on experience with cloud platforms (AWS preferred)Experience with distributed data processing frameworks (e.g., Spark, Databricks, Hadoop)Experience with orchestration and ETL tools such as Airflow, AWS Glue, Qubole, or similarStrong understanding of data modeling, system integration, and performance optimizationProven track record of shipping tools users actually adoptExperience working with Databricks, Redshift, Presto, or similar technologiesFamiliarity with ML pipelines and collaboration with data science teamsK