This is an opportunity to lead a flagship engineering effort at scale - one that will directly impact how users and developers interact with AI.

What you will own:

- Architect and lead the development of a large-scale, production-grade LLM management system - integrating external models, internal ML models, and data pipelines through Model Context Protocol (MCP).

- Design and implement robust, scalable, and maintainable AI systems, ensuring they meet performance, reliability, and cost-efficiency requirements.

- Design intelligent query routing algorithms that adapt in real time to query types, minimize latency, and maximize accuracy.

- Set up and enforce guardrails to ensure compliance with global data privacy laws, security standards, and ethical AI practices.

- Empower developers by building productivity tools such as Eval frameworks, IDE plug-ins, and scripting interfaces that accelerate AI solution development.

- Collaborate with data science teams to integrate custom ML models that enhance the system's capabilities.

- Establish a continuous feedback loop - using insights from users and system analytics to fine-tune model orchestration over time.

- Continuously evaluate emerging AI technologies, frameworks, and research breakthroughs. Drive adoption of innovative techniques to maintain competitive advantage

What you will bring:

- 15+ years of engineering leadership experience, including leading large-scale, distributed, high-availability platforms.

- A proven ability to architect and scale complex systems - ideally involving AI/ML, LLM APIs, or model orchestration.

- Deep technical expertise in cloud infrastructure, real-time systems, and API integrations.

- A strong track record of building secure, compliant, and ethical systems at scale.

- Passion for empowering teams - you know how to inspire engineers, data scientists, and product managers toward a common goal.

- Excellent communication and stakeholder management skills; you can balance strategic vision with hands-on leadership.

- Curiosity to stay ahead of the curve on emerging AI technologies and industry trends.

- Results-driven with a strong focus on achieving results.

- Thrives in a fast-paced, dynamic environment.

Stakeholder Management and Communication:

- Communicate AI Impact: Effectively articulate AI strategies, technical concepts, and project progress to diverse stakeholders, including senior leadership, product teams, and business leaders

- Align AI Initiatives with Business Goals: Translate complex AI capabilities into clear business value propositions and measurable outcomes, ensuring AI efforts drive revenue growth, user engagement, and operational excellence.

- Demonstrated ability to build and lead high-performing engineering teams.

Technical Skills:

- Deep understanding of transformer architectures, NLP, generative AI, and LLM technologies.

- Experience with cloud-based AI services and infrastructure (AWS, Azure, GCP).

- Knowledge of MLOps, model lifecycle management, and scalable AI system design.

- Familiarity with big data processing and integration of AI models into enterprise systems.