AI Engineer (Agentic AI / Voice Bot / LLM Engineering) (Gurugram)

Role & Responsibilities AI/ML Engineer (47 Years Experience) Role & Responsibilities We are looking for a hands-on AI/ML Engineer to build and deploy intelligent AI solutions across our communication platform. This is an individual contributor role involving development of LLM-powered applications, voice AI capabilities, NLP pipelines, and production-grade ML systems that enable automation and real-time decision-making. Key Responsibilities - Build and deploy AI/ML solutions using Large Language Models (LLMs), NLP, speech AI, and generative AI technologies. - Develop LLM-powered features including summarization, sentiment analysis, intent detection, auto-disposition, escalation tagging, and agent assistance. - Build and implement Agentic AI workflows, prompt pipelines, and retrieval-based systems using frameworks like LangChain. - Work with speech technologies including STT, TTS, Whisper, and voice intelligence solutions. - Fine-tune, evaluate, and optimize AI models for accuracy, latency, and scalability. - Build AI inference services and APIs using Python, FastAPI, Docker, and cloud-native technologies. - Develop and maintain ML pipelines for data processing, model training, evaluation, and continuous improvement. - Integrate AI models into product workflows such as communication platforms, CRM systems, dialers, and automation solutions. - Optimize AI systems for high-volume production environments with focus on performance and reliability. - Work with data technologies such as PostgreSQL, Redis, Kafka, and build scalable data workflows. - Implement secure and compliant AI data handling, retrieval, and access control mechanisms. - Collaborate with product, engineering, and business teams to deliver impactful AI-driven features as a hands-on builder. Ideal Candidate - Strong AI Engineer (Agentic AI / Voice Bot / LLM Engineering) Profiles - Mandatory (Experience 1) Must have 4+ years of overall software engineering experience, with at least 3+ years of hands-on experience in Artificial Intelligence, Machine Learning, Generative AI, or Applied AI engineering roles. - Mandatory (Experience 2) Must have robust hands-on experience designing, developing, and deploying AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, and Generative AI architectures. - Mandatory (Experience 3) Must have minimum 1+ year of recent hands-on experience in Voice Bot Development, Voice AI, Conversational AI, AI Voice Agents, Speech AI, Contact Center Automation, or Voice Automation Platforms. - Mandatory (Experience 4) Must have strong expertise in Python and should have built scalable AI/ML applications, APIs, microservices, or backend systems using Python-based frameworks - Mandatory (Experience 5) Must have hands-on experience with AI/LLM frameworks such as LangChain, LangGraph, Hugging Face, LlamaIndex, CrewAI, AutoGen, Whisper, OpenAI SDKs, or equivalent GenAI development frameworks - Mandatory (Notice Period) Immediate Joiners or candidates who can join within 15 days will be highly preferred. - Preferred (Cloud) Experience with AWS, Azure, GCP, MLOps, AI deployment platforms, model serving infrastructure, and cloud-native architectures. Skills: speech,python,automation,bot,ml,pipelines Role & Responsibilities AI/ML Engineer (47 Years Experience) Role & Responsibilities We are looking for a hands-on AI/ML Engineer to build and deploy intelligent AI solutions across our communication platform. This is an individual contributor role involving development of LLM-powered applications, voice AI capabilities, NLP pipelines, and production-grade ML systems that enable automation and real-time decision-making. Key Responsibilities - Build and deploy AI/ML solutions using Large Language Models (LLMs), NLP, speech AI, and generative AI technologies. - Develop LLM-powered features including summarization, sentiment analysis, intent detection, auto-disposition, escalation tagging, and agent assistance. - Build and implement Agentic AI workflows, prompt pipelines, and retrieval-based systems using frameworks like LangChain. - Work with speech technologies including STT, TTS, Whisper, and voice intelligence solutions. - Fine-tune, evaluate, and optimize AI models for accuracy, latency, and scalability. - Build AI inference services and APIs using Python, FastAPI, Docker, and cloud-native technologies. - Develop and maintain ML pipelines for data processing, model training, evaluation, and continuous improvement. - Integrate AI models into product workflows such as communication platforms, CRM systems, dialers, and automation solutions. - Optimize AI systems for high-volume production environments with focus on performance and reliability. - Work with data technologies such as PostgreSQL, Redis, Kafka, and build scalable data workflows. - Implement secure and compliant AI data handling, retrieval, and access control mechanisms. - Collaborate with pro