Lead Agentic Delivery Engineer

Lead the design, implementation, deployment, and support of AI/ML, analytics, automation, and generative AI solutions for client programs. Translate business and functional requirements into technical plans, solution components, and production-ready workflows. Oversee the development of data pipelines, model integration, validation processes, and other scalable technical assets. Manage delivery workstreams and guide teams in using Python, SQL, cloud platforms, and modern data tools to solve business problems. Define and monitor KPIs related to solution quality, model performance, data health, adoption, and business impact. Mentor junior practitioners and partner with client and internal stakeholders to drive execution, communicate progress and risks, and promote responsible, repeatable delivery practices.

What you'd actually do

  1. Lead the design, implementation, deployment, and support of AI/ML, analytics, automation, and generative AI solutions for client programs
  2. Translate business and functional requirements into technical plans, solution components, and production-ready workflows
  3. Oversee the development of data pipelines, model integration, validation processes, and other scalable technical assets
  4. Manage delivery workstreams and guide teams in using Python, SQL, cloud platforms, and modern data tools to solve business problems
  5. Define and monitor KPIs related to solution quality, model performance, data health, adoption, and business impact

Skills

Required

  • Python
  • SQL
  • cloud platforms
  • modern data tools
  • prompt engineering
  • LLM APIs
  • orchestration
  • RAG architectures

Nice to have

  • AWS
  • Azure
  • GCP
  • Databricks
  • Snowflake
  • BigQuery
  • dbt
  • Airflow
  • MLOps practices
  • deployment pipelines
  • monitoring
  • experiment tracking
  • model governance
  • containerization
  • BI or visualization tool
  • responsible AI practices
  • model transparency
  • bias considerations

What the JD emphasized

  • AI/ML, analytics, automation, and generative AI solutions
  • production, operational, or client-facing environments
  • prompt engineering, LLM APIs, orchestration, and RAG architectures

Other signals

  • Generative AI solutions
  • LLM APIs
  • orchestration
  • RAG architectures