Role & responsibilities

1. AI Opportunity Discovery & Business Analysis

- Conduct in-depth assessments of workflows, pain points, and data assets across all organisational functions to identify viable AI use cases.
- Develop a standardised AI opportunity canvas and scoring model for consistent evaluation of use cases.
- Interview senior stakeholders, process owners, and domain experts to surface latent transformation opportunities.
- Map existing technology landscape to identify integration points and gaps for AI enablement.

2. Generative AI Strategy & Roadmap

- Design a multi-horizon GenAI strategy covering quick wins (06 months), medium-term pilots (618 months), and long-term transformation programs.
- Evaluate and recommend foundation models, LLM platforms (e.g., OpenAI GPT, Anthropic Claude, Google Gemini, Meta LLaMA, Mistral), and deployment approaches (SaaS, fine-tuned, RAG-based, agentic).
- Define the AI architecture blueprint including model selection, vector databases, embedding strategies, prompt engineering standards, and agent orchestration frameworks.
- Develop a responsible AI policy covering data privacy, bias mitigation, explainability, and regulatory compliance.

3. COE Operations & Governance

- Build and lead a cross-functional COE team comprising AI engineers, data scientists, ML Ops engineers, and business analysts.
- Establish COE operating rhythms including sprint planning, use-case reviews, steering committee reporting, and retrospectives.
- Create reusable AI accelerators, prompt libraries, RAG pipelines, evaluation frameworks, and toolkits for scaling deployments.
- Define KPIs and success metrics for all AI initiatives; maintain dashboards for leadership visibility.

4. Implementation & Delivery Oversight

- Lead proof-of-concept design, prototype development, and production deployment of priority AI solutions.
- Oversee MLOps practices including model versioning,



monitoring, drift detection, and retraining pipelines.
- Partner with IT, InfoSec, Legal, and Compliance to ensure AI systems meet enterprise security and regulatory standards.
- Manage vendor relationships with AI platform providers, system integrators, and research partners.

5. Capability Building & Change Management

- Design and execute AI literacy programs and advanced training curricula for business users, data teams, and leadership.
- Champion a culture of experimentation, continuous learning, and data-driven decision-making.
- Facilitate internal communities of practice for AI/ML, sharing learnings and promoting reuse.
- Develop communication frameworks to manage change, address AI adoption resistance, and build organisational confidence.

Preferred candidate profile

- Minimum 5 years of hands-on experience specifically in Generative AI, including LLMs, prompt engineering, RAG architectures, and agentic systems.
- Proven track record of delivering enterprise-scale AI/ML solutions across multiple business domains.
- Strong understanding of the end-to-end AI lifecycle: ideation, data preparation, model development, deployment, monitoring, and iteration.
- Experience leading cross-functional teams and managing senior stakeholder relationships.
- Deep familiarity with at least two major GenAI platforms or APIs (e.g., OpenAI, Anthropic, Azure OpenAI, Google Vertex AI, AWS Bedrock).

Preferred Qualifications

- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related engineering discipline.
- Experience establishing or scaling an AI/ML COE or innovation lab within a large enterprise.
- Knowledge of multi-modal AI systems including text, image, audio, and video models.
- Exposure to AI regulation frameworks such as India's AI governance guidelines, or ISO/IEC 42001.
- Publications, patents, or significant contributions to open-source AI projects are a solid advantage.

📌 Lead Artificial Intelligence (Delhi)
🏢 Findi India
📍 Delhi