Lead the development and deployment of cutting-edge AI/ML solutions, with a special emphasis on Generative AI, Large Language Models (LLMs), and intelligent agents. This role involves architecting scalable, cloud-native AI systems and guiding enterprise-wide AI strategy.

Shape the future of AI within the organization by mentoring teams, defining AI priorities with executive leadership, and contributing to go-to-market initiatives. Continuous learning and community engagement are key to staying at the forefront of AI advancements.

Architect, build, and deploy production-grade AI/ML models, focusing on GenAI, LLMs, and intelligent agents. Provide architectural leadership across cross-functional teams for AI-enabled applications.

Design scalable, cloud-native AI solutions using OCI and other multi-cloud platforms. Mentor teams on AI solution design, best practices, and delivery excellence.

Guide enterprise-wide AI architecture strategy, ensuring alignment with data, DevOps, and security. Engage with C-suite stakeholders to define AI priorities and roadmaps.

Lead solution estimation, technical governance, and program oversight. Contribute to GTM initiatives, including demos and proposals.

Stay current with advancements in GenAI, LLM optimization, AI agent architectures, and model integration. Publish whitepapers and present at leadership forums.

Requires 12-15 years of overall experience, with at least 5 years in Data Science or AI/ML Engineering. Demonstrated expertise in leading end-to-end AI/ML solution design and production deployment is essential.

Proven track record in Generative AI, LLMs, NLP, and AI agent development. Proficiency in Python and modern ML/NLP frameworks like HuggingFace, LangChain, PyTorch, and TensorFlow is required.

Experience in fine-tuning LLMs for specific applications (e.g., Q&A systems, auto-documentation, predictive insights) is crucial. Hands-on experience building, scaling, and optimizing AI systems on OCI, including knowledge of hybrid/multi-cloud architectures, is necessary.

Strong understanding of machine learning algorithms, prompt engineering, vector databases, and RAG pipelines. Familiarity with MLOps, model governance, and CI/CD for ML workflows is expected.

Excellent communication and stakeholder management skills are vital for bridging technical and executive teams.