Must Have Technical/Functional Skills
Seeking a highly accomplished Principal AI Engineer to lead the design, architecture, and delivery of enterprise scale AI and Machine Learning solutions. This role requires deep technical expertise in AI/ML, strong architectural vision, hands-on leadership, and the ability to drive AI strategy across the organization. The ideal candidate has extensive experience with advanced ML systems, GenAI/LLM solutions, and end to end AI platform development.
Key Responsibilities
AI Strategy & Technical Leadership
• Define and drive the organization’s AI roadmap, architecture, and long-term technical vision.
• Lead and mentor AI/ML engineers, data scientists, and cross-functional teams.
• Evaluate emerging technologies and guide adoption of GenAI, LLMs, MLOps frameworks, and AI platforms.
AI/ML Architecture & Innovation
• Architect scalable, high-performance machine learning and GenAI solutions for NLP, CV, predictive analytics, and automation.
• Design and optimize LLM-powered systems, including RAG pipelines, fine-tuned models, and enterprise AI assistants.
• Develop reusable AI frameworks, components, and internal toolkits to accelerate organization-wide AI development.
End-to-End AI System Development
• Oversee full lifecycle: data architecture, feature engineering, experimentation, training, validation, deployment, and monitoring.
• Build large-scale, cloud-native AI systems using Azure/AWS/GCP.
• Establish standards for model quality, reliability, interpretability, and governance.
MLOps & Platform Engineering
• Architect robust MLOps pipelines—CI/CD, model versioning, drift detection, automated retraining, monitoring.
• Lead the development of AI platforms supporting multiple business teams and workloads.
• Ensure production environments meet performance, scalability, and security requirements.
Cross Functional Collaboration
• Partner with executives, product teams, and business leaders to identify AI opportunities.
• Translate complex business problems into scalable AI solutions.
• Represent AI architecture in technical reviews and decision-making processes.
• Databricks, SQL, and data processing frameworks.
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