About the role
Kapture CX is an AI-native customer experience platform helping enterprises turn their messiest service operations into seamless, intelligent experiences. We build cutting-edge AI (agentic automation, voice AI, real-time intelligence) for industries where complexity is the default: banking, financial services, retail, ecommerce and beyond.
Headquartered in Bangalore with a growing presence in the US and Dubai, we serve 1,000+ enterprise clients across 18+ countries. We’ve raised $20M to date, we’re profitable, and we’re growing 100% year on year, a combination that’s rare at the intersection of AI and SaaS.
Today we’re a 400+ strong team, and we’re looking for people who want to build AI that actually ships, scales and solves real problems.
What is this role all about?As a Gen AI Engineer, you will design, build and ship the LLM-powered agents and pipelines that run enterprise customer conversations across voice, chat, email and messaging. You will turn real customer workflows into reliable, measurable AI behavior that works at scale.Sounds interesting?Here’s a more detailed description of what you will do in this role:
- Design and build LLM-based agents, including prompt strategies, tool/function calling, memory and multi-step orchestration for customer support, sales, collections and claims workflows.
- Build and optimize RAG pipelines: ingestion, chunking, embeddings, vector search, re-ranking and grounding, so agents answer accurately from enterprise knowledge.
- Create evaluation frameworks (offline test sets, LLM-as-judge, regression suites, human review loops) to measure accuracy, hallucination, latency and cost.
- Integrate agents with CRMs, ERPs, ticketing systems and third-party APIs so they can complete tasks end to end.
- Build guardrails, escalation logic and human-in-the-loop handoffs for sensitive or low-confidence situations.
- Monitor production agents, analyze conversation failures, and continuously improve prompts, models and retrieval.
- Evaluate and select foundation models, and apply fine-tuning or distillation where it improves quality or cost.
- Work with Product, Engineering, CX Operations and Solutions teams to translate customer requirements into scalable AI capabilities.
- Stay current on LLM, agentic and speech/voice AI developments, and bring practical improvements into the platform.
What does success look like in this role?Your agents resolve customer queries accurately and safely at scale, with measurable gains in automation rate, resolution quality and cost per conversation. Issues are caught by your evaluation and monitoring before customers notice them, and new AI capabilities move from prototype to production quickly and reliably.
What would make you a good fit for this role?
- 2–5 years of software engineering experience, with at least 1 year building LLM/Gen AI applications in production.
- Strong Python skills and experience building backend services and APIs.
- Hands-on experience with LLM APIs (OpenAI, Anthropic, Gemini or open-source models) and frameworks or patterns for agents, tool calling and RAG.
- Working knowledge of vector databases, embeddings and search/retrieval techniques.
- Experience with prompt engineering and with evaluating LLM output quality.
- Exposure to conversational AI, chatbots, voice AI or automation platforms is a strong plus.
- Familiarity with cloud platforms, containers and CI/CD.
- Bachelor’s degree in Computer Science, Engineering or a related field.
- Building and debugging production-grade LLM agents and RAG systems
- Designing evaluations and iterating on measurable quality
- Structured problem solving and ownership from prototype to production
- Clear communication with technical and business stakeholders
- Comfort working in a fast-moving, customer-driven environment
- Hands-on exposure to large-scale CX automation and AI-powered platforms.
- Real production scale: enterprise agents handling millions of customer interactions.
- Direct impact on a product used by leading global brands.
- Close collaboration with product, engineering and customer teams.
- Learning and growth in a rapidly evolving agentic AI space.