About the job
About the Role
As the Director of AI Engineering at datawow, you will spearhead the AI Engineering division, driving the implementation and scalability of AI solutions across large-scale enterprise deployments. Your leadership will define our technical vision while you cultivate and mentor a high-performing team, ensuring that our AI initiatives evolve from concepts into robust, production-ready systems that yield significant business results.
This role combines strategic oversight with hands-on technical involvement. You will manage several enterprise projects, establish system architecture standards, and guarantee the excellence of our delivery processes.
Your responsibility will encompass the comprehensive success of AI deployment at scale, overseeing everything from technical direction and team capability to achieving desired customer outcomes.
Key Responsibilities
AI Engineering Leadership
- Develop and execute the overarching AI engineering strategy, including architecture standards and best practices.
- Recruit, mentor, and lead a talented team of AI engineers across various initiatives.
- Implement scalable delivery processes, coding standards, and quality benchmarks.
- Foster a strong engineering culture prioritizing ownership, efficiency, and impact.
Enterprise AI Delivery Oversight
- Manage the end-to-end delivery of AI solutions for enterprise clients.
- Ensure successful transitions from discovery through design to deployment and adoption.
- Serve as the escalation point for complex technical and delivery challenges.
- Collaborate closely with clients and stakeholders to ensure solutions meet their real business needs.
Agentic AI & System Architecture
- Define architectural patterns for multi-agent systems, orchestration, and AI workflows.
- Guide teams in developing reliable, production-ready agentic systems (e.g., LangChain, LangGraph).
- Ensure adherence to best practices in LLM orchestration, RAG pipelines, evaluation, monitoring, and human-in-the-loop designs.
Technical Strategy & Innovation
- Stay at the cutting edge of AI advancements (e.g., LLMs, agents, infrastructure) and convert them into actionable use cases.
- Assess tools, frameworks, and infrastructure to standardize practices across teams.
- Drive internal innovation through reusable frameworks, templates, and tools.
Product & Cross-functional Impact
- Recognize patterns across enterprise projects and transform them into product opportunities.
- Collaborate with Product and Engineering leaders to influence the roadmap.
- Facilitate the development of internal platforms, reusable assets, and accelerators.
Operational Excellence
- Establish KPIs for delivery success (e.g., adoption, performance, ROI).
- Ensure systems are scalable, observable, and maintainable.
