AI Practice Lead – Trainer (AI Solutions & Client Engagement)

About the role

We are looking for an experienced AI Practice Lead – Trainer to drive client engagements and develop custom AI solutions across multiple industry verticals. This executive‑level position combines deep expertise in Generative AI, Machine Learning, and Deep Learning with strong client‑management and strategic business skills.

Key responsibilities

• Lead high‑stakes client interactions, delivering executive presentations on AI strategy, ROI, and digital transformation roadmaps.
• Identify and qualify AI opportunities across BFSI, Healthcare, Manufacturing, Retail and emerging technology domains through consultative selling.
• Architect end‑to‑end AI solutions, including Generative AI, traditional ML, Deep Learning, and Large Language Models.
• Design AI frameworks with built‑in explainability, interpretability, and transparency mechanisms.
• Develop AI governance policies covering ethics, bias detection, fairness metrics, and security architecture.
• Lead proof‑of‑concept development, technical validation, and large‑scale implementation of AI systems.
• Define and execute AI practice strategy, service offerings, capability development, and market positioning.
• Establish partnerships with research institutions, technology vendors, and startups.
• Mentor and build high‑performing AI teams across technical and business functions.

Required profile

• 15+ years of experience in AI/ML, with at least 8 years in leadership or practice‑lead roles.
• Proven track record of client engagement and business development at the C‑suite level.
• Willingness to travel extensively for client meetings, solution presentations, and global AI conferences.
• Experience delivering AI solutions in BFSI, Healthcare, Manufacturing, Retail or related sectors.

Required skills

• Generative AI (GPT variants, diffusion models, multimodal generative systems)
• Machine Learning and Deep Learning techniques
• Large Language Model architecture, domain adaptation, fine‑tuning, prompt engineering
• Ensemble methods, reinforcement learning, advanced ML algorithms
• AI governance, ethical AI guidelines, bias detection, fairness metrics
• Security for AI systems, adversarial attack protection, privacy preservation