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About AiSensy
AiSensy is a WhatsApp based Marketing & Engagement platform helping businesses like Adani, Delhi Transport Corporation, Yakult, Godrej, Aditya Birla Hindalco, Wipro, Asian Paints, India Today Group, Skullcandy, Vivo, Physicswallah, and Cosco grow their revenues via WhatsApp.
Role Overview
We are looking for a Senior Machine Learning Engineer to lead the development and deployment of cutting-edge AI/ML systems with a strong focus on LLMs, Retrieval-Augmented Generation (RAG), AI agents, and intelligent automation.
You will work closely with cross-functional teams to translate business needs into AI solutions, bringing your expertise in building scalable ML infrastructure, deploying models in production, and staying at the forefront of AI innovation.
Key Responsibilities
AI & ML System Development
Infrastructure, Deployment & MLOps
Data & Feature Engineering
Team Collaboration & Technical Leadership
Required Qualifications
Preferred Qualifications
Why Join AiSensy
Ready to build intelligent systems that redefine communication
Apply now and join the AI revolution at AiSensy.
Job ID: 126866771
Skills:
Java Spring, Python, AWS, embeddings, LLM concepts, classical ML frameworks, prompt design, modern ML tooling
Skills:
Machine Learning, Nlp, Pytorch, Docker, Rest Apis, Kubernetes, Python, Hugging Face Transformers, RAG, vector databases
Skills:
Kubernetes, Computer Vision, Deep Learning, Rust, Jax, Video Processing, AWS, Tensorflow, Python, Azure, Gcp, Docker, Pytorch, GANs, Inpainting methods, Image processing techniques, Image-to-image generation, Image-to-video generation, 3D computer vision, VAEs, CNNs, Generative AI, Diffusion models
Skills:
Pandas, Numpy, AWS, Pytorch, Python, Docker, LLM core concepts, Airflow, MLflow, scikit-learn, ML data libraries, prompt design, agentic design patterns
Skills:
Tensorflow, Django, Hadoop, React, Kafka, AWS, Pytorch, Node.js, Kubernetes, Python, Azure, Algorithms, Docker, Gcp, Spark, data structures, scikit-learn, Airflow, generative AI technologies, Vector Databases, RAG architectures, transfer learning, prompt engineering
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