AI Engineer III

We are looking for engineers to build production-grade agentic AI systems that transform customer service experience across servicing channels. This role focuses on delivering measurable customer and operational outcomes through intelligent automation—not just building models.

Responsibilities

• Design, build, and deploy LLM-powered and agentic AI systems for real-time customer interactions across voice, chat, and messaging channels.

• Develop intelligent agents that:

• Understand customer intent

• Reason over context (interaction history, sentiment, policies)

• Invoke enterprise tools (CRM, knowledge bases, ticketing systems)

• Execute actions in real time and recover gracefully from failures

• Architect and implement scalable RAG pipelines over customer data, knowledge bases, and operational systems, ensuring:

• High accuracy and low hallucination rates

• Compliance and auditability

• Strong data privacy and PII handling

• Build and extend shared AI platforms, including:

• Conversational AI services

• Agent orchestration frameworks

• Real-time agent assist systems

• Evaluation, monitoring, and observability tooling

• Own end-to-end system performance, including:

• Reliability and fault tolerance

• Low-latency response constraints (real-time systems)

• Cost efficiency at scale

• Partner closely with product, operations, and CX design teams to deliver measurable outcomes such as:

• Reduced resolution time

• Increased containment

• Improved CSAT

• Enhanced agent productivity

Technical Environment

We operate in a modern, enterprise-scale environment focused on real-time, customer-facing AI systems. Strong fundamentals matter more than exact tool matching.

Core Engineering Stack

• Languages: Python, Go, TypeScript

• Cloud & Infrastructure: AWS and/or GCP, Kubernetes

• APIs: REST, gRPC

• Distributed Systems: Event-driven architectures (e.g., Kafka)

• Datastores: Postgres, MongoDB

• Vector Databases: Pinecone, Weaviate, FAISS

• Async Processing: Celery, Kafka

• Deployment: FastAPI, Docker, serverless

• Observability: LangSmith, Weights & Biases, Helicone

Agentic AI & Machine Learning (CX Focus)

• Hands-on experience integrating commercial and open-source LLMs into production workflows

• Experience building:

• Agent orchestration systems for multi-step interaction handling

• RAG pipelines over structured and unstructured enterprise data

• Semantic search systems using vector databases

• Evaluation frameworks (accuracy, hallucination, compliance, CX metrics)

• Strong practices in:

• Conversation state management

• Schema and tool interface design

• Guardrails, validation, and safe execution in regulated environments

AI-Assisted Development

• Fluency with AI-assisted development workflows (code generation, testing, evaluation, iteration)

• Ability to use these tools effectively while maintaining production-grade engineering standards

System Expectations

All systems must meet enterprise standards for:

• Reliability in high-volume, real-time environments

• Security and data privacy (including PII handling)

• Auditability of automated decisions and interactions

• Responsible AI deployment in regulated customer contexts

Qualifications

• 5+ years of software engineering experience, including production systems involving LLMs, conversational AI, or applied ML

• Proven track record of building and deploying AI-powered systems in customer-facing or operational environments (e.g., chatbots, agent assist, automation)

• Strong engineering fundamentals across:

• Backend systems

• APIs

• Data pipelines

• Cloud infrastructure

• Hands-on experience with modern LLM tooling and agentic architectures

• Strong ownership mindset and ability to operate in ambiguous problem spaces

• Product-oriented thinking with focus on customer experience and operational impact

Preferred Qualifications

• Experience in regulated industries (financial services, healthcare, telecom)

• Experience in high-growth or transformation environments

• Track record of deploying systems that directly impact customer interactions at scale

• Contributions to open-source projects in AI, conversational systems, or developer tooling