Convo & Agentic AI Developer -Senior Associate
On-siteBengaluru, Karnataka, India
Job Summary
Design and maintain intents, dialog flows, and NLU models for voicebots and chatbots on CCaaS platforms like NICE CXone, Genesys Cloud, and Amazon Connect. Configure IVR call flows, tune grammar settings, and write conversation scripts that handle edge cases and graceful escalations. Build LLM-powered agents capable of multi-step reasoning and tool invocation, implementing RAG pipelines to connect agents to CRM and backend systems. Orchestrate multi-agent workflows using LangChain or Semantic Kernel, applying guardrails and evaluation frameworks to ensure accuracy and safety. Monitor bot performance metrics, continuously retrain models with production utterances, and troubleshoot root causes for performance degradation. Collaborate with solution architects and DevOps teams to deploy, test, and optimize conversational solutions in production environments. Own end-to-end bot lifecycles from design through go-live, documenting flows and integration architectures for knowledge continuity.
Required Qualifications
- B. Engg./ B. Tech., M. Engg./ M. Tech.
- 5 - 8 years of experience
- Oral and written proficiency in English
- Hands-on experience with at least one CCaaS platform: NICE CXone, Genesys Cloud/PureCloud, Amazon Connect, Five9, Avaya, Twilio Flex, or similar
- Practical experience building chatbots/voicebots using NLU engines such as Google Dialogflow, Amazon Lex, Microsoft Bot Framework/CLU, IBM Watson Assistant, or Nuance Mix
- Working knowledge of LLMs and agentic frameworks (OpenAI/Anthropic APIs, LangChain, LangGraph, Semantic Kernel, or equivalent) and prompt engineering fundamentals
- Understanding of RAG architecture, vector databases (Pinecone, Weaviate, FAISS), and basic API/webhook integration (REST, JSON)
- Familiarity with a scripting/programming language (Python, JavaScript/Node.js) for custom logic, integrations, or agent tooling
- Strong analytical skills to interpret conversation logs, transcripts, and bot analytics dashboards to identify improvement areas
- Clear written and verbal communication for documenting flows and collaborating with cross-functional teams
- Proven track record independently owning at least one end-to-end conversational or agentic AI deployment from design through go-live
- Experience defining evaluation metrics/test suites for LLM agent quality (accuracy, hallucination rate, task success rate)
- Prior experience mentoring junior engineers or reviewing others' bot builds/prompt designs
- Proficiency in software development using Java, JavaScript, Python, or similar programming languages
- Hands-on experience in front end programming using JavaScript, HTML, CSS, and modern frameworks such as React or Angular
- Strong experience in API development and REST API integration, including authentication mechanisms and cloud-based services
- Experience designing and building microservices and event-driven architecture
- Strong grounding in solution design, DevOps collaboration, and defect management practices
- Experience with speech analytics, sentiment analysis, or real-time agent-assist tools
- Knowledge of telephony protocols (SIP, WebRTC) and voice biometrics
- Exposure to MLOps/LLMOps practices for monitoring and continuously improving deployed models/agents
- Relevant certifications (e.g., Genesys Cloud Certified, NICE CXone, AWS Certified – Machine Learning/Connect, Google Dialogflow)
- Experience working in SAFE Agile / Agile / Scrum development environments
- Familiarity with CI/CD pipelines, DevOps practices, and cloud-native development
Desired Qualifications
- Preference for at least one of the following fields of study: Management Information Systems, Computer and Information Science, Systems Engineering, Mathematics, Engineering, Electrical Engineering, Chemical Engineering, Industrial Engineering, Mathematics, Statistics, or Mathematical Statistics, Data Processing/Analytics/Science, Artificial Intelligence and Robotics
- At least one of the following: Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials
- Utilizing Python and Java for AI implementation and data modeling
- Applying machine learning libraries like TensorFlow and Scikit-Learn
- Developing complex data analysis and pattern recognition skills
- Implementing natural language processing techniques for text analytics
- Excelling in teamwork and communication within fast-paced environments
- Familiarity with CI/CD pipelines, DevOps practices, and cloud-native development is a plus
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