Senior Data Science SME - Training & Project Enablement

We are seeking a highly experienced and versatile Senior Data Science Subject Matter Expert (SME) to drive innovation, lead technical training, and provide essential project support for our advanced AI/ML initiatives. This role requires a blend of deep hands-on expertise across traditional AI, RAG, LLMs, and Agentic AI, coupled with proven experience in Cloud MLOps environments (specifically AWS, Azure, and GCP).

Key Responsibilities:

Subject Matter Expertise & Technical Leadership:

- Act as the internal SME, providing technical guidance and design oversight for projects leveraging Traditional Machine Learning, Generative AI, and Agentic AI systems.

- Maintain deep, up-to-date knowledge and hands-on proficiency across key cloud ML/AI services on AWS, Azure, and GCP.

- Define and enforce best practices for MLOps pipelines, including model versioning, testing, deployment, monitoring, and scaling.

- Collaborate with Data Scientists and Engineers to architect robust, scalable, and production-ready AI solutions.

Training & Mentorship:

- Design, develop, and maintain high-quality, practical training materials covering foundational data science concepts, advanced GenAI/LLMs/RAG, and Cloud MLOps best practices.

- Deliver engaging and effective technical training sessions to diverse audiences, including data scientists, engineers, product managers, and business stakeholders.

- Provide mentorship to junior and mid-level team members to elevate the organization's technical capabilities.

Project Support & Governance:

- Provide targeted, on-demand technical support and troubleshooting for active AI/ML projects.

- Create and manage clear, concise technical documentation and reference architectures.

- Assist project teams in selecting and configuring appropriate tools within the AWS, Azure, and GCP ecosystems.

Required Qualifications:

- Education: Master's degree or higher in Computer Science, Data Science, Engineering, or a related quantitative field.

- Experience: 9+ years of progressive experience in Data Science, Machine Learning Engineering, or MLOps, with at least 2 years in a dedicated technical training, mentorship, or SME role.

- AI/ML Expertise: Deep practical experience with the full ML lifecycle, including Traditional ML, Generative AI (LLMs, RAG), and Agentic AI frameworks.

- Cloud & MLOps: Expert-level proficiency in at least two of the three major cloud providers (AWS, Azure, GCP) and strong hands-on experience with MLOps tools like Kubernetes, Docker, MLflow, Vertex AI, or SageMaker.

- Communication: Exceptional written and verbal communication skills.

Preferred Certifications:

- AWS Certified Machine Learning Specialty

- Microsoft Certified: Azure Data Scientist or AI Engineer Associate

- Google Cloud Professional Machine Learning Engineer