- Deliver client-ready AI solutions : Design, prototype, and deploy at least two production-grade AI/ML models (e.g., marketing portfolio optimization, audience segmentation, content personalization) that drive measurable business outcomes.

- Build scalable RAG systems : Architect and launch a Retrieval-Augmented Generation (RAG) platform that unlocks global knowledge accessibility, improving efficiency by at least 30%.

- Unify fragmented data : Collaborate with data teams to break down silos and establish a scalable, cross-agency AI data platform within the first year.

- Productize internal AI tools : Scale the AI-assisted Input Brief initiative into a network-wide capability with 50% adoption across agencies by year one.

- Establish AI infrastructure : Develop cloud-based, secure, and version-controlled AI pipelines to ensure scalability, monitoring, and compliance with global data privacy standards.

- Drive innovation & influence : Introduce at least two innovations from emerging AI/ML research into enterprise applications, while translating business challenges into impactful AI-powered solutions.

Desired Qualifications :

- 8+ years in software engineering or data science, with 3+ years in applied AI/ML and a proven track record of delivering production-ready AI solutions.

- Hands-on experience building RAG systems with frameworks such as LangChain, LlamaIndex, and vector databases like Pinecone or Chroma.

- Advanced proficiency in Python and AI/ML libraries (TensorFlow, PyTorch, scikit-learn, Hugging Face).

- Strong expertise in NLP and unstructured data processing.

- Experience with at least one major cloud platform (AWS, GCP, Azure) for AI deployment.

- Skilled in designing complex AI workflows, including meta-agents, API integrations, and large-scale data transformations.

- Ability to communicate complex technical concepts clearly to non-technical stakeholders.

- Familiarity with MLOps principles and tools (e.g., MLflow, Kubeflow) is a strong plus.

- Experience in marketing, advertising, or consulting is advantageous but not required.