Technical Support Specialist providing technical relationship management and support for US clients in a leading marketing platform. Collaborating with Customer Success Managers to ensure partner satisfaction.
About the role
- Staff AI Engineer designing AI-powered automation solutions to enhance customer support operations. Leading technical initiatives and collaborating with cross-functional teams in a hybrid environment.
Responsibilities
- Design and deploy production-grade AI/ML systems that automate and augment customer support workflows.
- Build and scale LLM-powered solutions including RAG architectures, intelligent ticket routing, sentiment analysis, predictive insights, and automated response generation.
- Develop conversational AI systems and agentic workflows for autonomous case handling.
- Identify high-impact AI leverage points across support operations and translate them into measurable automation initiatives.
- Continuously evaluate emerging foundation models and AI tooling in the market; benchmark performance, cost, safety, and latency to assess where we stand and recommend adoption when strategically justified
- Own the full lifecycle of AI initiatives from ideation through deployment, monitoring, and optimization.
- Establish and mature MLOps practices including model versioning, observability, retraining pipelines, and cost-performance governance.
- Architect scalable APIs and microservices for AI integration across CRM, ticketing, and knowledge platforms.
- Partner with Product, Support Operations, Data Engineering, Security, and Leadership teams to deliver measurable business outcomes.
- Communicate technical architecture decisions and ROI clearly to executive stakeholders
Requirements
- 7+ years of software engineering experience, including 4+ years in AI/ML.
- Strong hands-on experience with Python and modern ML frameworks (PyTorch, TensorFlow, Hugging Face or equivalent)
- Proven experience building and deploying NLP/LLM-based systems in production.
- Hands-on experience designing and implementing RAG architectures and working with vector databases.
- Experience deploying ML systems in cloud environments (AWS experience, including Bedrock, is a strong advantage but not mandatory)
- Demonstrated ability to identify where AI can augment, automate, or eliminate operational workflows.
- Experience building scalable APIs, microservices, and containerized applications (Docker, Kubernetes)
- Strong understanding of MLOps, monitoring, and production observability.
- Experience operating in enterprise environments with compliance considerations (SOC2, GDPR, etc.)
- AI/ML certifications (AWS ML Specialty or equivalent) preferred
- Strong executive communication skills and ownership mindset
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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