Home/Jobs/AIML Engineer AIML Engineer Cyfuture India Pvt Noida 3-6 years Today $14.5K–24.1K/yr Full time Onsite Skills Required LLM RAG LangChain OpenAI LlamaIndex Agentic AI Prompt Engineering Haystack Machine Learning AI Python TensorFlow PyTorch Speech-to-Text Text-to-Speech Description Cyfuture India Pvt. Ltd. is hiring an AI/ML Engineer for onsite work in Noida. The role focuses on building scalable AI voicebot and LLM-based systems.

Company: Cyfuture India Pvt. Ltd.

Role: AIML Engineer Location: Noida, India (Onsite) Experience 3–6 yrs

3+ years of hands-on experience in Artificial Intelligence and Machine Learning

Experience building, deploying, and optimizing machine learning models

Experience working with large datasets Qualification B.Tech

M.Tech

MCA

Computer Science

Data Science

AIML related field

Tier 1 preferred Responsibilities Design and develop AI-powered voicebots for inbound and outbound calling use cases

Build and orchestrate agentic workflows using LLMs and external tool integrations

Integrate STT and TTS systems for real-time voice interactions

Develop multi-step reasoning pipelines using frameworks like LangChain

Implement context management, memory, and conversational state handling

Optimize LLM prompts for accuracy, latency, and token efficiency

Build and expose REST/MCP APIs for AI service integration

Ensure scalability, monitoring,



and observability of AI pipelines Nice To Have GPU-based inference and optimization More Skills Speech-to-Text (STT), Text-to-Speech (TTS), REST APIs, MCP APIs, Docker, Kubernetes, RAG (Retrieval-Augmented Generation), Agentic workflows, Tool calling, Evaluation techniques, GPU servers, AWS, GCP, Azure Other Employment Type: full time

Salary: ₹1,200,000 – ₹2,000,000

Openings: 3

Deadline: 8/30/2026

Company location: NSEZ, Noida

Company Website:, cyfuture.ai Prepare for this role Recommended resources to build the skills for this position. Sponsored. Generative AI with Large Language Models Coursera Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.

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