Sfl AI Technical Project Lead – AI Delivery & Executive Reporting

This role is for an AI Technical Project Lead responsible for the day-to-day execution of AI and consulting initiatives. The role involves translating business priorities into technical work, managing cross-functional technical teams, and providing executive-level communication and reporting. Key responsibilities include technical program management, coordination of AI/GenAI and data workstreams, sprint planning, risk management, and facilitating decision-making for clients and internal leaders.

What you'd actually do

  1. Collaborate with technical leaders on end-to-end delivery for AI/GenAI and data workstreams across one or more client engagements, managing scope, timeline, dependencies, budget and quality
  2. Responsible for overall coordination, status reporting, and stability of project-orientated work efforts within a program
  3. Translate business requirements into user stories, technical tasks and drive backlog hygiene (acceptance criteria, prioritization, sizing, and definition of done)
  4. Partner with architects, engineers, MLOps, and data science teams to validate feasibility, sequencing, and integration dependencies (e.g., LLM integration, RAG, evaluation, model deployment)
  5. Create weekly leadership updates (status, key decisions, risks, mitigations, and asks) tailored to executive audiences

Skills

Required

  • AI/GenAI project delivery
  • Technical program management
  • Agile methodologies (sprints, standups, retros)
  • Risk management (RAID)
  • Stakeholder management
  • Executive communication and presentation skills
  • Understanding of LLM integration, RAG, evaluation, and model deployment

Nice to have

  • Data strategy development
  • Data and AI governance frameworks
  • Organizational change management

What the JD emphasized

  • AI/GenAI
  • LLM integration
  • RAG
  • evaluation
  • model deployment

Other signals

  • AI/GenAI delivery
  • technical coordination
  • executive communication
  • translate business priorities into well-scoped work
  • manage delivery across cross-functional technical teams
  • LLM integration
  • RAG
  • evaluation
  • model deployment