Artificial Intelligence Evangelist

Required Skills & Experience

• 12–18 years in IT, with 5–8+ years in AI/ML and data-driven solution delivery.
• Strong knowledge of Machine Learning, Deep Learning, Generative AI (LLMs), RAG, and AI agent-based solutions.
• Proven experience in designing and delivering scalable AI/GenAI solutions across enterprise environments.
• Hands-on exposure to Python, AI/ML frameworks (TensorFlow, PyTorch), and LLM ecosystems (OpenAI, Azure OpenAI, Hugging Face).
• Experience with Azure/AWS/GCP AI services, cloud-native architectures, and API-first integrations.
• Working knowledge of model lifecycle management, deployment pipelines, monitoring, and observability.
• Strong understanding of data platforms, pipelines, vector databases, APIs, and enterprise integrations.
• Awareness of Responsible AI, model governance, data privacy, and compliance frameworks.
• Experience in leading AI programs, PoCs, and full lifecycle delivery from ideation to production.
• Ability to engage with business leaders, translate requirements, and influence decision-making.
• Exposure to solutioning, RFPs, client presentations, and AI advisory engagements.
• Proven ability to mentor teams, drive collaboration, and build AI competencies.

Responsibilities

• Drive enterprise AI adoption by defining strategy, use cases, and value-driven transformation roadmaps.
• Lead design and governance of scalable AI/GenAI solutions ensuring robust, secure, and high-performing implementations.
• Build and mentor high-performing AI teams while strengthening organizational AI capabilities and skills.
• Partner with business and technology leaders to translate needs into impactful AI-driven solutions.
• Oversee end-to-end AI program delivery ensuring timely execution and measurable business outcomes.
• Drive AI innovation and thought leadership through emerging tech adoption and industry engagement.
• Establish and enforce responsible AI practices, governance, and compliance frameworks.
• Ensure effective AI operations through KPIs, monitoring, lifecycle management, and continuous improvement.
• Lead AI initiatives and teams with strong decision-making, cross-functional collaboration, and technical ownership.
• Build team capabilities and foster continuous learning, innovation, and contribution to enterprise AI initiatives.