About The Role
We are looking for an AI/ML Engineer with 4+ years of robust hands-on experience building and deploying production-grade AI systems. The ideal candidate should have strong expertise in Computer Vision, Generative AI, RAG pipelines, Voice AI, and LLM-powered applications, with experience delivering scalable, production-ready solutions across enterprise, healthcare, or SaaS environments.

Key Responsibilities
• Design, develop, and deploy production-ready AI/ML and Generative AI applications, Computer Vision solutions.
• Build and optimize LLM-powered applications for real-world business use cases.
• Develop and maintain RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models.
• Build Voice AI solutions including speech-to-text (STT), text-to-speech (TTS), conversational AI, and voice-enabled assistants.
• Create and optimize prompt engineering strategies to improve LLM output quality, consistency, and reliability.
• Work with open-source LLMs for inference, fine-tuning, evaluation, and deployment.
• Implement model monitoring, evaluation, guardrails, and performance optimization.
• Collaborate with DevOps and cloud teams for scalable deployments.
• Optimize AI systems for latency, cost, scalability, and production performance.
• Integrate AI services through APIs and enterprise applications. Required Skills & Experience
• 4+ years of experience in AI/ML engineering and production deployments.
• Strong experience in Python and AI/ML development.




• Hands-on experience with Generative AI and LLM applications.
• Strong hands-on experience in Computer Vision using Python and frameworks such as OpenCV, PyTorch, and TensorFlow.
• Experience with Computer Vision tasks such as object detection, image classification, image segmentation, and feature extraction.
• Familiarity with models and libraries such as YOLO, Detectron2, MMDetection, and torchvision.
• Experience with image preprocessing, data augmentation, model training, evaluation, and inference optimization.
• Exposure to OCR technologies such as Tesseract or EasyOCR and vision-based deep learning models is an advantage. RAG & LLM Pipeline Skills
• Vector Databases: Pinecone, Weaviate, FAISS, ChromaDB, Milvus.
• Prompt Engineering techniques for LLM optimization.
• Open Source LLMs: Llama, Mistral, Falcon, Gemma, DeepSeek, Qwen. Voice AI & Frameworks
• Speech-to-Text: Whisper, Deepgram, Google STT.
• Text-to-Speech: ElevenLabs, Azure Speech, Amazon Polly.
• Frameworks: LangChain, LangGraph, LlamaIndex, Hugging Face. Deployment & Infrastructure
• Docker, Kubernetes, CI/CD.
• AWS / Azure / GCP. Preferred Qualifications
• Experience delivering AI solutions for Healthcare, Enterprise, or SaaS platforms.
• Exposure to MLOps practices and model lifecycle management.
• Understanding of AI governance, security, and responsible AI practices. Education Qualifications
• B.
Tech / M.
Tech in CSE / IT / AI&ML; only from good college. (ref:hirist.tech)

📌 Quanteon Solutions (Hyderabad)
🏢 Quanteon Solutions
📍 Hyderabad