AI Head

AI Strategy & Technical Leadership

• Own the AI roadmap and define the technical vision and strategy for RevRag AI's AI function.
• Work closely with the CTO and product leadership to translate business and customer requirements into a clear, prioritized AI roadmap.
• Evaluate emerging AI models, research, frameworks, and technologies and determine where they can create meaningful product or business advantage.
• Make pragmatic technology decisions balancing model quality, engineering complexity, time-to-market, and cost.

Build & Lead the AI Team

• Hire, mentor, and retain high-caliber AI/ML engineers and researchers.
• Build a culture of strong ownership, technical rigor, experimentation, and continuous learning.
• Define technical standards and expectations across the AI team.
• Mentor senior engineers and researchers through architecture reviews, technical discussions, and complex problem-solving.
• Build an organization that can independently deliver high-quality AI systems at scale.

Stay Hands-On

• Stay deeply involved in architecture, model design, experimentation, evaluation, and code reviews.
• Personally contribute to solving the hardest technical problems when required.
• Review and guide model training, fine-tuning, inference, evaluation, and production architecture decisions.
• Ensure strong engineering and mathematical foundations are applied throughout the AI lifecycle.

Ship AI to Production

• Take AI systems from research and experimentation through deployment and production monitoring.
• Build reliable, scalable, low-latency AI systems with robust evaluation and monitoring loops.
• Establish processes for continuous model evaluation, iteration, and quality improvement.
• Drive improvements in model accuracy, reliability, latency, and overall production performance.

Model Quality & Evaluation

• Own the approach to model training, fine-tuning, benchmarking, and evaluation.
• Establish robust evaluation methodologies and quality metrics for AI systems.
• Use strong mathematical, statistical, and experimental reasoning to guide model and system improvements.
• Ensure model performance is measured consistently across relevant production use cases and edge cases.

Infrastructure & Cost

• Own key decisions around AI infrastructure, compute, model serving, and tooling.
• Understand GPU/compute requirements, inference architecture, throughput, latency, and scalability trade-offs.
• Build systems that maintain healthy unit economics as usage scales.
• Track and optimize metrics such as cost per call, cost per token, and cost per minute.

Cross-Functional Leadership

• Work closely with Product, Engineering, and Revenue teams to translate customer and business needs into AI capabilities.
• Partner with founders and leadership to identify new opportunities where AI can create significant value.
• Help balance customer requirements with long-term platform and technology priorities.

Set AI Engineering Standards

• Establish best practices for data quality, experimentation, model evaluation, security, monitoring, and responsible AI development.
• Build strong review and documentation practices across the AI function.
• Ensure AI systems are designed with reliability, observability, and maintainability from the beginning.

Experience

• 8+ years of experience in AI/ML with a strong track record of building and shipping AI systems into production.
• Proven experience leading an AI/ML team, including hiring, mentoring, and delivering outcomes through others.
• Experience operating in high-growth or fast-paced environments with significant technical ownership.
• Strong understanding of the full AI lifecycle, from experimentation and model development through production deployment and optimization.

Technical Skills

• Strong hands-on understanding of Machine Learning, Deep Learning, and modern AI systems.
• Strong mathematical foundation across statistics, probability, optimization, and machine learning theory.
• Deep understanding of model training, fine-tuning, evaluation, inference, and deployment.
• Strong software engineering fundamentals and the ability to work closely with engineering teams on production systems.
• Strong understanding of AI infrastructure, compute, model serving, scalability, latency, and cost optimization.
• Ability to evaluate technical trade-offs across model quality, performance, infrastructure, and cost.

Leadership & Communication

• Strong track record of building and scaling high-performing technical teams.
• Clear and structured communicator who can work effectively with founders, executives, product leaders, and engineers.
• Strong technical judgment and ability to make decisions in ambiguous environments.
• High ownership, strong execution orientation, and the ability to operate at both strategic and technical depth.

Education

• Degree from a Tier 1 engineering or science institute.

Preferred Qualifications

Experience with any of the following will be highly valued:

• Building Voice AI products or systems.
• Speech recognition, text-to-speech, real-time voice agents, or conversational AI.
• Hands-on fine-tuning and adapting open-source or proprietary models.
• Building AI systems for BFSI, fintech, healthcare, or other regulated industries.
• Building AI platforms or teams from the ground up.

What We're Looking For

You're a strong fit if you:

• Have spent significant time building and shipping production AI, not just prototypes or research experiments.
• Can move comfortably between AI strategy, system architecture, model-level decisions, and hands-on execution.
• Understand that strong AI systems require more than just good models and can reason about the engineering and economic trade-offs involved.
• Have a strong mathematical foundation and use it to make better technical decisions.
• Enjoy working on difficult, ambiguous problems where there is no obvious reference implementation.
• Have high standards for technical quality and believe in rigorous evaluation, experimentation, and engineering discipline.
• Build teams that are autonomous, accountable, and technically strong.
• Can translate customer and business problems into scalable AI solutions without over-engineering.

Success in This Role

You'll be successful if you:

• Build a strong, well-run AI team with clear ownership and a high technical bar.
• Establish a clear AI roadmap aligned with RevRag AI's product and business priorities.
• Ship AI systems that are accurate, reliable, low-latency, and production-ready.
• Establish measurable evaluation and quality frameworks with continuous improvement over time.
• Improve the scalability and reliability of RevRag AI's AI infrastructure.
• Ensure compute and infrastructure spending scales sensibly with usage and revenue.
• Establish RevRag AI as a technically strong and execution-focused AI organization.