





Niche ML/LLM skillset and seniority, but smaller employer reduces applicant density.
Core ML/AI skills transfer across industries, but contact-center and speech analytics increase domain specificity.
Explicit 7+ years and many mandatory ML, MLOps, cloud, and language technology requirements.
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Lead design and development of scalable AI/ML solutions for enterprise-grade contact center products, including end-to-end ownership from model training to deployment.
Drive AI innovation by researching and applying advanced technologies like Generative AI, RAG, embeddings, and multimodal models, while integrating AI capabilities into cloud-native microservices.
Mentor and guide R&D team members on ML concepts and engineering practices; collaborate across functions to align AI development with strategic business needs and support demos, POCs, and roadmap discussions.
7+ years of hands-on experience in software engineering and applied AI/ML development with solution delivery.
Proficiency in Python and at least one other programming language (C# or JavaScript).
Experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn and working knowledge of NLP, LLM fine-tuning, Generative AI ecosystems, and MLOps practices.
Familiarity with cloud platforms (AWS/GCP), containerization (Docker/Kubernetes), microservices architecture, and strong foundation in data structures, software architecture, and distributed systems.
Experienced technical lead capable of managing end-to-end AI/ML product development in enterprise environments, especially in contact center or similar analytics domains.
Strong applied knowledge of modern AI technologies including GenAI, LLMs, and real-time data analytics, with ability to align AI capabilities to business-driven measurable impacts.
A collaborative leader who actively mentors and drives innovation while making informed technical decisions integrating AI into cloud-native architectures and products.