Director of Engineering – Decision Intelligence Platform

AT&T AT&T · Telecom · Dallas, TX

Director of Engineering to lead the strategy, architecture, build, and scaling of an AI/ML-driven Decision Intelligence platform for enterprise decisioning (Next Best Action/Offer), journey management, and real-time personalization. The role involves building a robust data and model foundation, a reusable decisioning layer, and low-latency activation services. Requires strong leadership in managing engineering, data, and ML teams, with a focus on cloud-scale distributed systems, real-time decisioning, and MLOps.

What you'd actually do

  1. Set vision and roadmap for an AI/ML-driven Decision Intelligence platform enabling NBA/NBO, journey orchestration, and real-time personalization across channels.
  2. Build and lead a high-performing org across software, data, ML, and analytics; drive a platform culture of reusable components, productized APIs, self-service tooling, and operational excellence.
  3. Deliver core platform capabilities: batch/streaming pipelines, real-time signals ingestion, identity/profile, feature pipelines (online/offline parity), model serving and monitoring (drift), decision engine (models + rules + constraints), and orchestration/activation integrations.
  4. Own reliability and compliance: privacy-by-design, data integrity, auditability, observability, CI/CD, SRE practices, and 24x7 SLAs/SLOs (incident response, runbooks, resilience testing).
  5. Implement closed-loop measurement and optimization through experimentation, performance reporting, and feedback pipelines to continuously improve decision policies and models.

Skills

Required

  • 15+ years in technology, including 8+ years leading cross-functional teams across engineering, data, and ML for customer-facing platforms.
  • 10+ years building cloud-scale data platforms (lake/warehouse, ETL/ELT, streaming).
  • Proven integration with MarTech/CDP/journey orchestration and activation ecosystems (Adobe/Salesforce/Marketo/HubSpot or similar), focused on real-time activation and personalization.
  • End-to-end ownership of platform architecture through production operations, including governance and lifecycle management.
  • Strong record of building high-performing teams (including managers of managers) and aligning business and technical stakeholders.
  • Product/platform mindset with disciplined prioritization to drive adoption and measurable customer outcomes.
  • Clear communicator who can explain NBA/NBO, experimentation, and model risk to non-technical audiences.
  • Ability to manage competing priorities while maintaining quality, security, and reliability.
  • Expertise in cloud-scale distributed systems and low-latency, high-throughput real-time decisioning (API-first, event-driven architectures; Azure/AWS/GCP).
  • Hands-on experience with key stack components: Adobe Experience Platform (AEP) (RT-CDP, AJO, Target, CJA), Streaming (Kafka, Flink, Spark or equivalent), Data platforms (Snowflake/lakehouse), strong SQL and NoSQL, Microservices (Java/Node), Kubernetes, API gateway/service mesh patterns, Observability & reliability (logs/metrics/traces, alerting, SLO/SLA management).
  • Deep understanding of Decision Intelligence patterns: NBA/NBO, orchestration, eligibility/constraints, frequency capping, and omnichannel policy consistency.
  • Proven production ML delivery: feature engineering, feature store parity, model building, model serving, MLOps, monitoring/drift, retraining, and experimentation (A/B, uplift; bandits where appropriate).
  • Strong modern engineering practices: Agile/SAFe, DevOps/MLOps, CI/CD, IaC, secure-by-design, and automa

Nice to have

  • privacy-enhancing techniques
  • bandits/experimentation

What the JD emphasized

  • AI/ML-driven Decision Intelligence platform
  • real-time personalization
  • low-latency activation services
  • model serving and monitoring
  • decision engine (models + rules + constraints)
  • real-time inference
  • low-latency, high-throughput real-time decisioning
  • model building, model serving, MLOps, monitoring/drift, retraining, and experimentation

Other signals

  • AI/ML-driven Decision Intelligence platform
  • real-time personalization
  • low-latency activation services
  • model serving and monitoring
  • decision engine (models + rules + constraints)