Data Science/ML Engineer

• Design enterprise GenAI solutions using LLMs, RAG, Agentic AI, and Machine Learning.
• Work with business stakeholders to identify AI opportunities and translate requirements into scalable architectures.
• Develop POCs, MVPs, and prototypes to demonstrate technical feasibility and business value.
• Define AI architecture covering model selection, orchestration, vector databases, and deployment.
• Collaborate with engineering teams to transition approved solutions into production.
• Present AI solutions and architecture recommendations to technical teams and executive leadership.

• Strong Data Science/Machine Learning background, preferably someone who started their career in ML or Data Science before moving into architecture.
• Target 10 15 years of experience, rather than 20+ years.
• Hands-on experience architecting enterprise Generative AI solutions.
• Strong knowledge of Machine Learning, NLP, or Computer Vision.
• Experience with LLMs, RAG architectures, and Agentic AI frameworks.
• Experience building AI POCs and transitioning successful solutions toward production.
• Strong Python skills and familiarity with ML/Deep Learning frameworks.
• Experience with AI technologies such as LangChain, LangGraph, LlamaIndex, vector databases, and cloud AI platforms.
• Excellent communication, presentation, and stakeholder management skills.

Nice to Haves:

• Experience working within an AI Center of Excellence (CoE).
• Experience with Google Vertex AI, AWS SageMaker, or Hugging Face.
• Experience leading enterprise AI transformation initiatives.
• Background across NLP, Computer Vision, and Predictive Analytics.
• Experience estimating ROI and presenting AI investment recommendations to leadership.