AI Architect
Oracle
Skills
- Python
- Generative AI
- LLMs
- NLP
- PyTorch
- TensorFlow
- HuggingFace
- LangChain
- MLOps
- RAG
- Vector Databases
- OCI
- System Architecture
- Stakeholder Management
About the role
Qualifications & Experience
• 12–15 years of total experience, with 5+ years in Data Science or AI/ML Engineering roles.
• Demonstrated experience leading end-to-end AI/ML solution design and production deployment.
• Proven track record in Generative AI, LLMs, NLP, and AI agent development.
• Expert in Python and familiar with modern ML/NLP frameworks: HuggingFace, LangChain, PyTorch, TensorFlow, etc.
• Experience fine-tuning LLMs for domain-specific applications (e.g., Q&A systems, auto-documentation, predictive insights).
• Hands-on experience building, scaling, and optimizing AI systems on OCI, with knowledge of hybrid/multi-cloud architectures.
• Strong knowledge of machine learning algorithms, prompt engineering, vector databases, and RAG pipelines.
• Familiar with MLOps, model governance, and CI/CD for ML workflows.
• Strong communication and stakeholder management skills—able to bridge the gap between technical and executive teams.
Key Responsibilities
• Architect, build, and deploy production-grade AI/ML models with a strong focus on GenAI, LLMs, and intelligent agents.
• Serve as a lead architect across multiple cross-functional teams delivering AI-enabled applications.
• Design scalable, cloud-native AI solutions using Oracle Cloud Infrastructure (OCI) and other multi-cloud platforms.
• Mentor teams on solution design, best practices, and delivery excellence for AI projects.
• Guide enterprise-wide AI architecture strategy, ensuring alignment with data strategy, DevOps, and security best practices.
• Engage with C-suite stakeholders to define AI priorities, value propositions, and roadmaps.
• Lead solution estimation, technical governance, and program oversight.
• Contribute to GTM initiatives, including customer-facing demos and proposal development.
• Stay current with the latest advancements in GenAI, LLM optimization, AI agent architectures, and model integration strategies.
• Publish internal whitepapers, present at leadership forums, and help grow the AI practice through coaching and community engagement.
Career Level - N/A
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