AI COE Head

Key Responsibilities:

Technical & Hands-on Leadership:

• Lead end-to-end design, development, and deployment of AI/ML solutions across use cases (NLP, CV, predictive analytics, GenAI, recommendation systems).
• Be hands-on with model development, experimentation, training, evaluation, and optimization.
• Architect scalable AI platforms and pipelines (data ingestion, feature engineering, model lifecycle management, MLOps).
• Drive adoption of GenAI/LLMs (prompt engineering, fine-tuning, RAG architectures, AI agents).

CoE Setup & Governance:

• Establish the AI CoE operating model, standards, and best practices.
• Define AI governance, model risk management, ethics, security, and compliance frameworks.
• Create reusable accelerators, frameworks, and reference architectures.

Business & Stakeholder Collaboration:

• Partner with business leaders, product teams, and engineering to identify high-impact AI use cases.
• Translate business problems into AI roadmaps with clear ROI and measurable outcomes.
• Act as a thought leader and advisor on AI strategy across the organization.

Team & Capability Building:

• Build, mentor, and scale a high-performing team of data scientists, ML engineers, and AI engineers.
• Review code, models, and solution design to ensure quality and scalability.
• Upskill teams and drive an AI-first culture across the organization.

Required Skills & Experience:

Experience:

• 15-18 years of overall experience, with at least 1215 years in AI/ML, data science, or advanced analytics roles.
• Proven experience setting up or leading an AI CoE or large-scale AI programs.
• Strong track record of delivering production-grade AI solutions.

Technical Skills (Must-Have):

• Strong hands-on expertise in Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Deep understanding of ML algorithms, deep learning, NLP, and generative AI.
• Experience with LLMs (OpenAI, Anthropic, open-source models), fine-tuning, and RAG.
• Solid experience in MLOps, CI/CD for ML, model monitoring, and lifecycle management.
• Strong understanding of data engineering concepts, cloud platforms (AWS/Azure/GCP), and scalable architectures.

Nice to Have:

• Experience in building AI solutions for retail, B2B SaaS or enterprise platforms.
• Exposure to AI governance, regulatory compliance, and responsible AI frameworks.
• Prior startup or GCC experience is a plus.

Leadership & Behavioral Traits:

• Strong hands-on mindset with the ability to go deep into code and models.
• Strategic thinker with execution focus.
• Excellent communication skills and ability to influence senior stakeholders.
• Comfortable operating in ambiguity and building from zero to scale.