Keurig Dr Pepper is looking to lead cross-functional engineering teams delivering GenAI, agentic AI, RPA, AI/ML, and low-code/no-code capabilities across ~28k employees. This role is not hands-on coding but will focus on setting standards, directing execution, and ensuring secure, compliant, and measurable outcomes for these AI services.
Requirements
- Agentic/orchestration: LangChain, LlamaIndex, LangGraph, AutoGen/CrewAI
- MLOps/workflows: MLflow, Kubeflow, Airflow/Prefect, DVC, Feast
- Vectors/NLP: pgvector, FAISS/Milvus/Qdrant, Transformers/embedding libraries
- Automation/low-code OSS: Robot Framework, TagUI, n8n, Appsmith
- Observability: OpenTelemetry
- Expert knowledge of system dev. life cycle (SDLC) methodologies (e.g., waterfall, spiral, SAFe, agile, rapid prototyping, incremental, synchronize and stabilize and DevOps).
- Expert knowledge of software design concepts, application servers, middleware applications and other software-related tools and concepts.
Responsibilities
- Establish and enforce engineering standards for prompts/agents, safety, evaluations, telemetry, CI/CD, and change control.
- Stand up governed pathways for low-code/no-code (environments, DLP, connector approvals, promotion flows).
- Define reliability and cost guardrails for AI workloads (SLOs, incident playbooks, FinOps hygiene) and review service health.
- Build and maintain a reusable accelerator catalog (RAG/agent templates, bot/flow patterns).
- Guide modernization and tech-debt reduction; oversee upgrade plans and risk mitigation.
- Coach tech leads; partner with Security, Architecture Data, and Legal on compliance-by-design.
- Coordinate with internal platform teams and open-source communities to align on standards.
Other
- Translate business goals into prioritized roadmaps; run intake, planning, reviews, and release readiness across multiple squads.
- Proven leadership across multiple engineering teams in SDLC/Agile/DevOps at enterprise scale.
- Deep understanding of distributed systems, APIs/events, data practices, and AI/ML/agentic patterns (planning, tool-use, memory, evals).
- Experience governing RPA and low-code programs at scale.
- Security mindset (least privilege, data minimization, prompt-injection/exfiltration defenses).
