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About arenaflex – Pioneering Customer‑Centric Innovation
arenaflex is a global leader in delivering seamless, intelligent experiences that transform how millions of customers interact with brands online and offline. With a relentless focus on innovation, data‑driven decision making, and a deep‑rooted obsession for customer success, arenaflex has built a reputation for turning cutting‑edge research into real‑world impact at scale. Our Customer Engagement Technology (CET) division leverages conversational Artificial Intelligence (AI), Large Language Models (LLM), and advanced machine learning (ML) to anticipate, understand, and resolve customer issues before they become problems. By automating routine interactions and empowering support agents with powerful AI‑driven tools, we help our clients achieve faster resolutions, higher satisfaction scores, and measurable cost savings.
Why This Role Matters
As the Applied Science Manager for Customer Engagement Technology, you will own the vision and execution of arenaflex’s most ambitious AI‑driven transformation initiatives. You’ll lead a cross‑functional team of applied scientists, engineers, product managers, and UX designers to create multi‑modal, multi‑turn dialogue systems that operate in multiple languages and adapt instantly to evolving business policies. Your work will directly shape the future of conversational commerce, drive operational efficiency for millions of support agents, and set new industry standards for intelligent, human‑like interactions.
Key Responsibilities
- Technical Vision & Strategy: Define, communicate, and evolve the long‑term roadmap for AI‑powered customer experience transformation across arenaflex’s global services.
- Leadership of Dialogue Systems Development: Oversee the design, implementation, and scaling of multi‑modal, multi‑turn conversational agents that can handle high‑volume, multi‑language interactions.
- Foundational Model Innovation: Lead the creation of domain‑specific, multi‑modal foundational models that power both chat and voice experiences for customer service.
- Testing & Validation: Establish rigorous evaluation frameworks—including automated testing, bias detection, and performance monitoring—to ensure robustness, safety, and compliance.
- Team Mentorship & Growth: Provide day‑to‑day technical guidance, career coaching, and professional development opportunities for a high‑performing applied science team.
- Cross‑Functional Collaboration: Partner closely with Product Managers, UX Designers, Software Development Engineers, and Business Stakeholders to translate business goals into technical deliverables.
- Business‑Technical Alignment: Translate complex AI capabilities into clear, value‑driven outcomes for business owners, ensuring measurable impact on customer satisfaction and operational metrics.
- Technology Radar: Stay ahead of emerging AI/ML research, evaluate new techniques (e.g., Retrieval‑Augmented Generation, transformer efficiency, multimodal fusion), and integrate relevant innovations into arenaflex’s product stack.
- Cultural Champion: Foster a culture of curiosity, continuous learning, and inclusive excellence where diverse viewpoints shape groundbreaking solutions.
Essential Qualifications
- Minimum 3 years of leadership experience managing scientists, ML engineers, or applied research teams in a fast‑paced technology environment.
- Deep technical expertise in Machine Learning, Natural Language Processing (NLP), Information Retrieval, and Analytics—including hands‑on experience with large‑scale language models and multimodal systems.
- Proven track record of delivering production‑grade AI solutions that operate at scale, preferably in a customer‑facing or contact‑center context.
- Strong publication or patent record that demonstrates thought leadership in AI/ML research.
- Exceptional communication skills—able to articulate complex technical concepts to both technical and non‑technical audiences.
- Demonstrated ability to build and nurture high‑performing, diverse teams.
Preferred Qualifications
- Experience building end‑to‑end machine learning pipelines for business applications, from data ingestion to model serving and monitoring.
- Hands‑on experience developing complex software systems that incorporate deep learning, computer vision, and speech technologies.
- Familiarity with modern AI infrastructure (e.g., Kubernetes, TensorFlow, PyTorch, distributed training frameworks).
- Background in conversational AI product development, in