Senior Data Science SME, (Training & Project Enablement)

Role & responsibilities

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). The ideal candidate will bridge the gap between complex technical concepts and effective organizational understanding, ensuring successful deployment and adoption of cutting-edge AI solutions.

Key Responsibilities

Subject Matter Expertise & Technical Leadership

• Act as the internal SME, providing technical guidance and design oversight for projects leveraging Traditional Machine Learning (e.g., forecasting, classification), Generative AI (LLMs, RAG frameworks), 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 across multi-cloud environments.
• 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 (presentations, labs, documentation) covering foundational data science concepts, advanced GenAI/LLMs/RAG, and Cloud MLOps best practices on AWS, Azure, and GCP.
• Deliver engaging and effective technical training sessions to diverse audiences, including data scientists, engineers, product managers, and business stakeholders.
• Provide one-on-one and group mentorship to junior and mid-level team members, helping to elevate the organization's overall technical capabilities in AI and Cloud.

Project Support & Governance

• Provide targeted, on-demand technical support and troubleshooting for active AI/ML projects, ensuring adherence to architectural standards and efficient resource utilization.
• Create and manage clear, concise technical documentation, reference architectures, and internal knowledge bases to support project continuity and self-service.
• Assist project teams in selecting and configuring appropriate tools and services within the AWS, Azure, and GCP ecosystems to accelerate project delivery and maintain compliance.

Required Qualifications

• Education: Master's degree or higher in Computer Science, Data Science, Engineering, or a related quantitative field.
• Experience: 8+ 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 and proficiency in:
• Traditional ML: Scikit-learn, TensorFlow/PyTorch, statistical modeling.
• Generative AI: Implementing and fine-tuning LLMs, proficiency with RAG (Retrieval-Augmented Generation) architectures.
• Agentic AI: Understanding of AI Agents, planning, and tool-use frameworks.
• Cloud & MLOps: Expert-level proficiency in at least two of the three major cloud providers (AWS, Azure, GCP) and strong hands-on experience designing and managing MLOps pipelines using tools like Kubernetes/Docker, MLflow, Vertex AI, SageMaker, or Azure ML.
• Communication: Exceptional written and verbal communication skills, with a proven ability to simplify complex technical topics for non-technical audiences and develop high-impact training content.

Preferred Certifications (A Strong Plus)

• AWS Certified Machine Learning Specialty
• Microsoft Certified: Azure Data Scientist Associate or Azure AI Engineer Associate
• Google Cloud Professional Machine Learning Engineer

Preferred candidate profile

Must have minimum 8 years of experience in DS/AI/ML

Candidate below 8 years of experience, not accepted.