Lead Specialist, Data Science & Artificial Intelligence II
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Job Purpose
Lead and develop end-to-end machine learning and Generative AI solutions, ensuring architectural integrity, technical excellence, and business alignment while mentoring team members and driving successful delivery.
Key Accountabilities
• Lead and actively develop ML, deep learning, and Generative AI solutions.
• Design and implement LLM-based applications including RAG pipelines and embeddings strategies.
• Own solution architecture from problem framing to production deployment.
• Establish MLOps and LLMOps practices for deployment, monitoring, and retraining.
• Define evaluation standards, guardrails, and AI safety controls.
• Mentor team members and drive cross-functional collaboration.
• Ensure compliance with data governance and responsible AI standards.
Minimum Qualification, Experience and Competencies
• Minimum Qualification
• Bachelor’s degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative field
Minimum Experience
• 7–9 years of experience in data science and AI, including leading end-to-end ML and Generative AI initiatives.
Skills:
• Advanced ML & deep learning
• LLM architecture design
• API orchestration & system integration
• AI safety & risk mitigation
• Enterprise AI deployment
• Cloud AI platforms (Azure, AWS, GCP)
• Technical leadership & stakeholder engagement
• Solution architecture thinking
Requirements
- •Bachelor’s degree in Data Science, AI, Computer Science, or related quantitative field
- •7–9 years of experience in data science and AI
- •Experience leading end-to-end ML and Generative AI initiatives
- •Advanced ML and deep learning knowledge
- •LLM architecture design
- •API orchestration
- •System integration
- •Enterprise AI deployment
Nice to Have
- •Cloud AI platforms (Azure, AWS, GCP)
- •Technical leadership
- •Stakeholder engagement
- •Solution architecture thinking
Responsibilities
- •Lead and actively develop ML, deep learning, and Generative AI solutions
- •Design and implement LLM-based applications including RAG pipelines and embeddings strategies
- •Own solution architecture from problem framing to production deployment
- •Establish MLOps and LLMOps practices for deployment, monitoring, and retraining
- •Define evaluation standards, guardrails, and AI safety controls
- •Mentor team members and drive cross-functional collaboration
- •Ensure compliance with data governance and responsible AI standards
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