About the Role:

We are looking for an AI/ML Engineer with 4+ years of strong 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.