Head of AI
🧠 Head of AI — Roles and Responsibilities
1. AI Strategy & Roadmap
Define and own the company-wide AI vision aligned with product and business goals.
Develop short-term experiments and long-term AI capabilities roadmap.
Decide on build vs. buy decisions for AI systems and platforms.
2. Architecture & Technical Leadership
Design scalable AI/ML pipelines (e.g., LLMs, retrieval-augmented generation, MLOps, fine-tuning).
Make architectural decisions on models, data pipelines, vector DBs, inference APIs, etc.
Oversee infrastructure for model training, evaluation, and deployment.
Ensure model safety, privacy, and compliance in regulated environments.
3. AI/ML Product Development
Collaborate with Product to translate customer problems into AI-powered features.
Prioritize experiments based on feasibility, impact, and data availability.
Lead research and development for GenAI use cases (text generation, embeddings, summarization, etc.).
4. Team Building & People Leadership
Hire and lead cross-functional AI teams: ML engineers, data scientists, research engineers.
Set up mentoring, growth paths, and performance reviews.
Create a strong AI culture that emphasizes experimentation, ethics, and delivery.
5. MLOps & Productionization
Implement versioning, reproducibility, monitoring, and alerting for models in production.
Ensure CI/CD pipelines are in place for AI components.
Optimize inference for latency and cost (e.g., quantization, batching, async APIs).
6. Cross-Functional Collaboration
Work with engineering, product, and data teams to embed AI into the company’s core products.
Guide marketing/sales with technical content, customer education, and positioning.
Provide support to customer success and solutions teams for AI deployments.
7. Client & Stakeholder Engagement
Join strategic client conversations where AI is central to the offering.
Translate client requirements into custom model behavior or feature adaptations.
Co-develop POCs and guide enterprise onboarding from a tech perspective.
8. Research & Innovation
Stay ahead of trends in LLMs, diffusion models, multimodal AI, etc.
Publish internal whitepapers or contribute to external publications (blogs, conferences).
Evaluate and test open-source and proprietary models (GPT, Claude, Gemini, etc.).
9. Governance & AI Ethics
Implement responsible AI practices: bias detection, explainability, audit trails.
Maintain security, access control, and privacy compliance (GDPR, HIPAA, etc.).
Educate internal teams on ethical implications of AI deployment.
🔍 Optional KPIs / Success Metrics
Model deployment success rate
Uptime & latency of AI services
AI-driven feature adoption
POC-to-deal conversion (if client-facing)
Team NPS / retention
Technical debt reduction / infra cost optimization
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Director of AI & Client Strategy Roles and Responsibilities
Here’s a tailored breakdown of Director of AI & Client Strategy roles and responsibilities — a hybrid leadership role bridging AI innovation with enterprise go-to-market (GTM) success.
🎯 Director of AI & Client Strategy — Roles & Responsibilities
This role combines deep technical expertise in AI/ML/GenAI with strategic client engagement, focusing on revenue growth, solution customization, and long-term client value.
🧠 AI/ML Leadership & Technical Execution
AI Architecture
Lead design of scalable, production-grade AI systems (e.g., LLMs, RAG, custom NLP).
Model Strategy
Choose optimal models (OpenAI, Claude, open-source) based on use case and cost.
Delivery Oversight
Ensure successful execution of AI projects, from prototyping to production.
MLOps
Implement workflows for model versioning, retraining, monitoring, and governance.
Experimentation
Drive POC and A/B testing of AI features, ensuring they’re measurable and actionable.
🤝 Client-Facing Strategy & Enterprise GTM
Pre-sales Support
Join sales conversations to translate business problems into AI solutions.
Solution Engineering
Design architecture proposals, integration plans, and technical documentation.
Client Discovery
Lead deep-dive sessions with enterprise stakeholders to uncover strategic pain points.
POC to Production
Own the AI part of the client lifecycle from demo → pilot → scale.
Post-sale Enablement
Ensure clients can successfully adopt and derive value from AI solutions.
📊 Strategic Leadership & Business Impact
Product-Market Fit Loop
Serve as a feedback loop between enterprise clients and product/engineering.
Roadmap Alignment
Influence company-wide product and AI roadmap based on client demand patterns.
Revenue Contribution
Tie AI initiatives directly to top-line revenue through POCs, upsells, etc.
Partnership Development
Build alliances with cloud providers, LLM vendors, and system integrators.
Reporting & OKRs
Define and track AI performance and client success metrics.
👥 Cross-Functional Collaboration
Engineering
Align backend/data infra with AI integration plans.
Sales & BD
Craft sales collaterals, join strategic pitches, help close deals.
Customer Success
Enable post-sale client onboarding, adoption, and success through AI insights.
Founders/Execs
Provide insights on AI-driven revenue growth and market positioning.
✅ Success Metrics / KPIs
AI Impact
# of deployed models, accuracy/lift, cost savings from automation
Revenue Impact
POC conversion rate, new ARR contributed, enterprise expansion wins
Client Retention
NPS scores, AI-powered product adoption, reduction in churn through strategic use
Speed & Delivery
Time from idea to live AI solution (incl. POC cycle time)
Innovation
# of new AI use cases launched per quarter, patents/IP created
🛠️ Tools / Stack You Might Be Working With
LLMs & NLP: OpenAI (GPT), Claude, Gemini, LLaMA, Mistral
Infra: FastAPI, LangChain, AutoGen, Qdrant/Weaviate, Redis, Kubernetes
MLOps: Prefect, MLflow, Hugging Face, DVC, Docker, GitHub Actions
CRM/Client Tools: Pipedrive, HubSpot, Notion, Slack, Loom
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