Responsibilities
- Design and implement advanced AI/ML systems with a focus on LLMs, AI Agents, and retrieval-augmented generation (RAG) architectures.
- Build production-grade AI pipelines for data processing, model training, fine-tuning, and serving at scale.
- Build conversational AI interfaces that handle multi-turn customer interactions, maintain context across sessions, and seamlessly escalate to human agents when necessary.
- Implement feedback loops and continuous learning systems that incorporate customer satisfaction metrics, agent corrections, and conversation outcomes to improve model performance over time.
- Create analytics dashboards and reporting tools to track automation effectiveness, identify common customer pain points, and measure key performance indicators like resolution time, containment rate, and customer satisfaction scores.
- Lead technical initiatives for AI system integration into existing products and services.
- Collaborate with data scientists and ML researchers to implement and productionize new AI approaches and models.
Requirements
- Bachelor's degree in Computer Science, or a related field, or equivalent practical experience.
- 5+ years in backend software development using modern programming languages (e.g., Python (strongly preferred!), Golang or Java).
- Demonstrated experience building production conversational AI systems including chatbots, virtual assistants, and automated support agents using LLMs (OpenAI, Anthropic, open-source models).
- Expertise in natural language understanding (NLU) and intent classification for customer query interpretation, entity extraction, and conversation flow management.
- Experience implementing multi-channel support automation across chat, email, voice, and messaging platforms with consistent context handling.
- Strong background in customer support metrics and KPIs including CSAT, first contact resolution, average handle time, and containment rate optimization.
- Experience with sentiment analysis and emotion detection for escalation triggers and customer satisfaction monitoring.
- Expertise in building knowledge bases and FAQ systems with dynamic content retrieval and self-learning capabilities from support interactions.
- Proficiency with contact center platforms (Zendesk, Salesforce Service Cloud, Genesys, or similar) and their API integrations.
- Experience implementing real-time agent assist systems that provide suggestions, knowledge articles, and response templates during live interactions.
- Familiarity with compliance and security requirements for handling sensitive customer data in automated systems (PCI, HIPAA, GDPR).
- Experience with A/B testing and experimentation frameworks for optimizing conversation flows and response strategies.
