Description

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

Key Skills

Education

Any Gradute