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Senior AI/ML Engineer

Tirget Analystics

Hyderabad / Bengaluru

5-8 years

Today

$41.0K–60.2K/yr

Full-time

Onsite

Skills Required

Gen AI

Transformers

Embeddings

LLM

RAG

Prompt Engineering

Vector Database

LangChain

LlamaIndex

Hugging Face

Agentic AI

NLP

Natural Language Understanding

Conversational AI

Voice Analytics

Description

Senior AI/ML Engineer role focused on conversational AI and voice analytics. The position involves building production-grade NLP, speech, and generative AI solutions for enterprise use cases.

Role: Senior AI/ML Engineer – Conversational AI & Voice Analytics

Location: Hyderabad/Bangalore

Experience

5–8 years of experience
Strong hands-on experience in Natural Language Processing and Natural Language Understanding
Experience with intent classification, entity extraction, text classification, sentiment analysis, and semantic similarity
Experience building chatbots, conversational AI platforms, virtual assistants, or voicebots
Hands-on experience with ASR/STT and TTS technologies
Strong understanding of LLMs and Generative AI
Strong proficiency in Python
Experience deploying AI/ML solutions on Azure, AWS, or GCP

Responsibilities

Design, develop, and deploy production-grade NLP, NLU, and conversational AI solutions
Build intelligent chatbots, virtual assistants, and voicebots for multi-turn conversations and contextual interactions
Develop and optimize intent classification, entity recognition, dialogue management, and context-handling capabilities
Work with IVR and voice-based systems to automate customer interactions and improve conversational experiences
Build solutions using ASR/STT and TTS technologies
Analyze voice and call-center data to derive insights on customer sentiment, intent, conversation quality, and agent performance
Develop call transcription, summarization, sentiment analysis, conversation analytics, and automated quality monitoring capabilities
Leverage LLMs and Generative AI to enhance conversational experiences and voice analytics
Design and implement RAG-based conversational applications, prompt engineering, grounding, and hallucination mitigation
Explore and implement AI agents, tool/function calling, and agentic workflows for conversational use cases
Integrate AI models with enterprise applications, APIs, databases, and downstream systems
Evaluate and benchmark NLP, speech, and LLM models for accuracy, latency, cost, and business performance
Develop scalable APIs and production-ready AI services using Python and modern AI/ML frameworks
Collaborate with product, engineering, and business teams to translate requirements into scalable AI solutions
Ensure solutions are production-ready with monitoring, evaluation, security, and performance optimization

Nice To Have

Experience with platforms/frameworks such as Rasa, Dialogflow, Microsoft Bot Framework, or equivalent
Experience with IVR, Voice AI, speech analytics, or contact-center solutions
Experience with RAG, prompt engineering, embeddings, vector databases, and LLM evaluation
Experience with frameworks such as LangChain, LlamaIndex, or Hugging Face
Exposure to AI agents, function calling, and agentic workflows
Experience with Docker/Kubernetes and production-grade deployment
Understanding of model monitoring, observability, scalability, and performance optimization
Experience in
Experience with platforms such as Azure AI Speech, Azure OpenAI, Google Speech-to-Text, Google Dialogflow, Amazon Connect, or equivalent
Experience with multilingual conversational AI
Experience handling large-scale voice/call datasets
Experience fine-tuning or adapting LLMs, NLP, or speech models
Knowledge of responsible AI, privacy, and security considerations for conversational/voice data

More Skills

IVR, Voice Data Analysis, Intent classification, Entity extraction, Speech-to-Text, Text-to-Speech, Language models, Dialogue management, Multi-turn conversations, Rasa, Dialogflow, Microsoft Bot Framework, ASR, STT, TTS, Voice AI, Speech analytics, Contact-center solutions, Call transcription, Conversation analytics, Sentiment analysis, Emotion analysis, LLM evaluation, Function calling, Agentic workflows, Python, FastAPI, Flask, Databases, Data-processing frameworks, Git, Testing, Production deployment, Docker, Kubernetes, Model monitoring, Observability, Scalability, Performance optimization, Azure, AWS, GCP, Azure AI Speech, Azure OpenAI, Google Speech-to-Text, Google Dialogflow, Amazon Connect, Multilingual conversational AI, Large-scale voice/call datasets, Fine-tuning, Responsible AI, Privacy, Security

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