Ai/ML Engineer

1 day ago

Bengaluru, Karnataka, India Tamcherry Full time ₹ 20,00,000 - ₹ 25,00,000 per year


• Minimum 5 years of experience in machine learning engineering and AI development


• Deep expertise in machine learning algorithms and techniques like supervised/unsupervised learning, deep learning, reinforcement learning, etc.


• Solid experience in natural language processing (NLP) - language models, text generation, sentiment analysis, etc.


• Proven understanding of generative AI concepts like text/image/audio synthesis, diffusion models, transformers, etc.


• Expertise in Agentic AI and building real world applications using the same


• Experience working with Agentic AI frameworks like Langgraph , ADK , Autogen etc.


• Hands-on experience developing and deploying generative AI applications (text generation, conversational AI, image synthesis, etc.)


• Experience in MLOps and ML model deployment pipelines.


• Proficiency in programming languages like Python, and ML frameworks like TensorFlow, PyTorch, etc.


• Knowledge of cloud platforms (AWS, GCP, Azure) and tools for scalable ML solution deployment


• Experience with data processing, feature engineering, and model training on large datasets.


• Familiarity with responsible AI practices, AI ethics, model governance and risk mitigation


• Understanding of software engineering best practices and applying them to ML systems.


• Experience with an agile development environment.


• Exposure in tools/framework to monitor and analyse ML model performance and data accuracy.


• Strong problem-solving, analytical, and communication abilities


• Bachelor's or master's degree in computer science, AI, Statistics, Math or related fields

Responsibilities:


• Design, develop and optimize machine learning models for applications across different domains.


• Build natural language processing pipelines for tasks like text generation, summarization, translation, etc.


• Develop and deploy cutting-edge generative AI & Agentic AI applications


• Implement MLOps practices - model training, evaluation, deployment, monitoring, and maintenance.


• Integrate machine learning capabilities into existing products or build new AI-powered applications.


• Perform data mining, cleaning, preparation, and augmentation for training robust ML models.


• Collaborate with cross-functional teams to translate AI requirements into technical implementations.


• Ensure model performance, scalability, reliability.


• Continuously research and implement state-of-the-art AI/ML algorithms and techniques.


• Manage the end-to-end ML lifecycle from data processing to production deployment.

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