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AI & Innovation Director

Roles & Responsibilities

  • 5+ years in a product, engineering, or technology leadership role
  • Demonstrated track record of shipping AI/LLM-based tools or products into production
  • Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Google Gemini, or open-source equivalents) and common orchestration frameworks (LangChain, LlamaIndex, or similar)
  • Experience managing or mentoring a technical team and bridging tech-to-business communication

Requirements:

  • Lead the design, development, and deployment of LLM-powered tools—from internal automation to client-facing solutions—shipping AI products
  • Own the full product lifecycle (ideation to scaled rollout) with clear success metrics and measurable ROI
  • Build and lead a small AI-focused team, setting direction, prioritizing the roadmap, and fostering fast experimentation
  • Drive organizational adoption and governance: partner with departments, enable non-technical teams to use AI tools, and champion responsible AI practices

Job description

ABOUT THE ROLE 

ClearSource is looking for a Director of Innovation who has gone beyond experimenting with AI — someone who has shipped real products and tools powered by large language models (LLMs) inside a company. You will own our AI roadmap end-to-end: identifying high-impact use cases, building or overseeing the tools that bring them to life, and driving adoption across the business. 

This is a player-coach role. You will lead a small team while staying hands-on technically — scoping solutions, guiding architecture decisions, and rolling up your sleeves when needed. You report to a VP/SVP and will work cross-functionally with Operations, Technology, and Client Services to embed AI into the fabric of how ClearSource operates. 

WHAT YOU'LL DO 

Build & Ship AI Products 

  • Lead the design, development, and deployment of LLM-powered tools — from internal automation to client-facing solutions. 

  • Translate ambiguous business problems into scoped, buildable AI products with clear success metrics. 

  • Own the full product lifecycle: ideation → prototype → pilot → scaled rollout. 

Lead a Small Team 

  • Hire, manage, and develop a small team of AI engineers, analysts, or product specialists. 

  • Set technical direction, prioritize the roadmap, and hold the team accountable to delivery timelines. 

  • Foster a culture of fast experimentation — build, test, learn, iterate. 

Drive Organizational Adoption 

  • Partner with department heads to identify where AI can reduce cost, improve quality, or create new value. 

  • Build internal enablement programs so non-technical teams can leverage AI tools confidently. 

  • Champion responsible AI practices — governance, bias awareness, data privacy, and security. 

Stay Ahead of the Curve 

  • Monitor the LLM ecosystem (OpenAI, Anthropic, Google, open-source models) and evaluate new capabilities for business applicability. 

  • Build and maintain relationships with key AI vendors and platform partners. 

  • Report progress, ROI, and forward-looking strategy to senior leadership. 

WHAT WE'RE LOOKING FOR 

The most important thing: you have actually built and launched LLM-powered products inside a company — not just used AI tools, but built them. 

Required Experience 

  • 5+ years in a product, engineering, or technology leadership role. 

  • Demonstrated track record of shipping AI/LLM-based tools or products into production (not just proofs of concept). 

  • Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Google Gemini, or open-source equivalents) and common orchestration frameworks (LangChain, LlamaIndex, or similar). 

  • Experience managing or mentoring a technical team. 

  • Ability to bridge the gap between technical implementation and business stakeholder communication. 

Preferred Experience 

  • Background in BPO, contact center, or tech-services environments — you understand the operational context we work in. 

  • Experience with RAG (Retrieval-Augmented Generation), fine-tuning, or prompt engineering at scale. 

  • Familiarity with enterprise AI governance frameworks and responsible AI principles. 

  • Prior work in a multi-geo or global team environment. 

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