Experience:

• 5+ years of experience in AI/ML engineering, data science, or a related field

Qualification:

• Advanced certification in AI/ML or cloud platforms like Azure, AWS, or Google Cloud
• Microsoft Certified: Azure AI Engineer
• AWS Certified Machine Learning
• Google Professional Machine Learning Engineer

Responsibilities:

• Develop and leverage AI/ML models, including traditional predictive models and Generative AI models
• Implement Retrieval-Augmented Generation (RAG) techniques
• Analyze large-scale datasets
• Build and adapt models
• Ensure models are deployed into production effectively
• Build, fine-tune, and optimize supervised, unsupervised, reinforcement learning, and generative models
• Design models for NLU, dialogue management, knowledge retrieval, NER, intent classification, recommendation systems, and QA
• Implement advanced Gen AI models for dynamic content generation, chatbots, and contextual understanding
• Develop automated pipelines for model training, testing, and deployment
• Monitor and manage AI model health in production using AIOps techniques
• Ensure continuous improvement and retraining based on performance metrics and evolving data trends
• Collaborate with Data Engineers to build data pipelines, perform ETL, and preprocess large datasets
• Integrate AI models with external systems like SAP, ServiceNow, and other business-critical applications
• Partner with UI/UX Designers to integrate AI solutions into user-facing products
• Work with Full Stack Developers to integrate AI models into backend and frontend systems
• Engage with QA Engineers to validate model robustness and accuracy through rigorous testing protocols
• Develop and enforce guidelines for ethical, transparent, and unbiased models
• Implement data governance, model documentation, and compliance checks
• Conduct periodic reviews to ensure alignment with responsible AI practices
• Stay up-to-date with the latest advancements in AI/ML and generative AI technologies
• Experiment with emerging models and frameworks
• Drive thought leadership through internal knowledge sharing, AI workshops, and external publications

Nice to have:

• Azure preferred over AWS or Google Cloud
• Familiarity with Generative AI models such as GPT-3, DALL-E, BERT, etc., and their practical applications
• Experience with AIOps practices for automating model lifecycle management
• Knowledge of responsible AI, ethics, and bias mitigation in production environments

More skills:

AI/ML models, Retrieval-Augmented Generation (RAG), Google Cloud, Natural Language Processing (NLP), Computer Vision, dialogue management, Named Entity Recognition (NER), Natural Language Understanding (NLU), Question-Answering (QA), OCR, Weights & Biases (W&B), SAP, ServiceNow, ETL, data validation, entity resolution

Other:

• Trending