Role Summary:
We are looking for a highly experienced AI Architect specializing in Python-based AI development Large Language Models (LLMs) and the design of chatbots and voice bots. The ideal candidate will architect enterprise-grade conversational AI solutions ensure robust LLM performance monitoring and drive innovation in Generative AI systems.
Key Responsibilities
LLM & Conversational AI Architecture
Architect scalable solutions using LLMs ChatGPT-style models and voice AI frameworks.
Design and build chatbots and voice bots using Python ASR (Automatic Speech Recognition) TTS (Text-to-Speech) and NLP/LLM pipelines.
Create frameworks for conversational flows prompt engineering retrieval-augmented generation (RAG) and context management.
Solution Development
Build end-to-end AI applications using Python integrating with APIs databases and cloud-native services.
Develop modular and reusable components for LLM inference vector search embeddings and model orchestration.
Integrate LLMs with enterprise systems (CRM ticketing case management internal knowledge bases).
LLM Performance Monitoring & Optimization
Implement monitoring systems for latency hallucination rate safety compliance drift detection prompt performance and model quality.
Set up continuous evaluation (CEVAL) feedback loops and telemetry dashboards.
Optimize inference cost token usage model selection (small vs. large models) and caching strategies
Voice Bot & Chat Bot Engineering
Architect solutions using:
o Speech APIs (Azure Speech Amazon Transcribe Google Speech-to-Text)
o Chat platforms (Teams Slack web chat widgets)
o Telephony integrations (Twilio Genesys Ujet)
Ensure high accuracy in intent detection slot filling sentiment tracking and multimodal interaction.
MLOps & Deployment
Implement MLOps practices including CI/CD model versioning A/B testing evaluation pipelines and governance.
Deploy models on cloud platforms such as Azure AWS or GCP (Azure preferred if using OpenAI/Azure OpenAI).
Ensure compliance with enterprise AI governance security and ethical AI standards
Essential Skills: AI Architect Python LLM Conversational AI Architecture voice AI frameworks ASR (Automatic Speech Recognition) TTS (Text-to-Speech) and NLPLLM pipelines Voice Bot Chat Bot Engineering.
Role Summary: We are looking for a highly experienced AI Architect specializing in Python-based AI development Large Language Models (LLMs) and the design of chatbots and voice bots. The ideal candidate will architect enterprise-grade conversational AI solutions ensure robust LLM performance monitor...
Role Summary:
We are looking for a highly experienced AI Architect specializing in Python-based AI development Large Language Models (LLMs) and the design of chatbots and voice bots. The ideal candidate will architect enterprise-grade conversational AI solutions ensure robust LLM performance monitoring and drive innovation in Generative AI systems.
Key Responsibilities
LLM & Conversational AI Architecture
Architect scalable solutions using LLMs ChatGPT-style models and voice AI frameworks.
Design and build chatbots and voice bots using Python ASR (Automatic Speech Recognition) TTS (Text-to-Speech) and NLP/LLM pipelines.
Create frameworks for conversational flows prompt engineering retrieval-augmented generation (RAG) and context management.
Solution Development
Build end-to-end AI applications using Python integrating with APIs databases and cloud-native services.
Develop modular and reusable components for LLM inference vector search embeddings and model orchestration.
Integrate LLMs with enterprise systems (CRM ticketing case management internal knowledge bases).
LLM Performance Monitoring & Optimization
Implement monitoring systems for latency hallucination rate safety compliance drift detection prompt performance and model quality.
Set up continuous evaluation (CEVAL) feedback loops and telemetry dashboards.
Optimize inference cost token usage model selection (small vs. large models) and caching strategies
Voice Bot & Chat Bot Engineering
Architect solutions using:
o Speech APIs (Azure Speech Amazon Transcribe Google Speech-to-Text)
o Chat platforms (Teams Slack web chat widgets)
o Telephony integrations (Twilio Genesys Ujet)
Ensure high accuracy in intent detection slot filling sentiment tracking and multimodal interaction.
MLOps & Deployment
Implement MLOps practices including CI/CD model versioning A/B testing evaluation pipelines and governance.
Deploy models on cloud platforms such as Azure AWS or GCP (Azure preferred if using OpenAI/Azure OpenAI).
Ensure compliance with enterprise AI governance security and ethical AI standards
Essential Skills: AI Architect Python LLM Conversational AI Architecture voice AI frameworks ASR (Automatic Speech Recognition) TTS (Text-to-Speech) and NLPLLM pipelines Voice Bot Chat Bot Engineering.
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