Responsibilities
- Design and implement advanced AI/ML systems with a focus on SLMs, LLMs, AI Agents, and Search architectures.
- Build conversational AI interfaces that handle multi-turn low latency chat/voice customer interactions, maintain context across sessions, and seamlessly escalate to human agents when necessary.
- Build production-grade AI pipelines for data processing, model training, fine-tuning, benchmarking (dual-control, fluid model etc.) and serving at scale.
- Implement feedback loops and continuous learning systems that incorporate customer satisfaction metrics, agent corrections, LLM evaluations, human evaluations and conversation outcomes to improve model performance over time.
- Reinforce organizational policies based on knowledge bases, conversational data and memory systems.
- Create AI based analytics dashboards and reporting tools to track automation effectiveness, tracing for identifying bottlenecks, identify common customer pain points, and measure key performance indicators like resolution time, containment rate, and customer satisfaction scores.
- Quality assurance, management to get actionable insights from customer conversations and create evals for the current generation agents.
- 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 AI systems including chatbots, virtual assistants, and automated support agents using SLMs, LLMs (commercial, open-source models).
- Demonstrated strong foundational understanding and appreciate first principles thinking in ML, NLP (transformer based models).
- Expertise in natural language understanding (NLU) and intent classification for customer query interpretation, entity extraction, dialogue state tracking and conversation flow management for building a reliable framework for context engineering.
- Expertise in tuning streaming ASR and TTS engines, Speech to Speech models for context and domain aware transcriptions and naturalness in voice.
- Expertise in conversation mining for identifying customer intents, root cause analysis, sentiment, resolution, policy adherence for not just auditing but truly understanding conversations for business outcomes across large enterprise scale.
- Expertise in working with use case based SLMs for realtime agent coaching and recommendations.
- Expertise in building knowledge bases and FAQ systems with dynamic content retrieval and self-learning capabilities from support interactions.
- Experience implementing multi-channel support automation across chat, email, voice, and messaging platforms with consistent context handling.
- Deep knowledge of REST API design, Pub-Sub architectures and integration patterns.
- Strong understanding of software architecture, scalability, security, and system design.
- Strong communication abilities to explain technical concepts.
- Collaborative mindset for cross-functional team work.
- Detail-oriented with strong focus on quality.
- Self-motivated and able to work independently.
- Passion for solving complex search problems.
Nice to Have
- Experience with PostgreSQL and ClickHouse, or similar relational and analytical databases (added advantage but not necessary skill).
- Experience with Docker, Kubernetes, and deploying to cloud environments (AWS, GCP, or Azure).
- Experience with A/B testing and experimentation frameworks.
Benefits
- Flexible, trust-oriented culture that empowers everyone to take full ownership of their roles.
- Vibrant and dynamic work environment.
- Multitude of benefits they can enjoy inside and outside of their work lives.
