Information Technology_USA - USA_Product Architect

Real Soft•Jacksonville, FL

6d•Onsite

About The Position

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.

Requirements

AI Architect
Python
LLM
Conversational AI Architecture
voice AI frameworks
ASR (Automatic Speech Recognition)
TTS (Text-to-Speech)
NLP/LLM pipelines
Voice Bot
Chat Bot Engineering
8-10 years of experience

Nice To Haves

Digital: Python
Digital: Machine Learning
Digital: Artificial Intelligence (AI)
Digital: Chatbots / Conversational Agents
AI Agents
AI & Gen AI - Products & Tools
Azure preferred if using OpenAI/Azure OpenAI

Responsibilities

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.
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).
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.
Architect solutions using Speech APIs (Azure Speech, Amazon Transcribe, Google Speech-to-Text), Chat platforms (Teams, Slack, web chat widgets), and Telephony integrations (Twilio, Genesys, Ujet).
Ensure high accuracy in intent detection, slot filling, sentiment tracking, and multimodal interaction.
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.