As an AI Engineer, you will be responsible for developing and leveraging AI/ML models, including both traditional predictive models and Generative AI models (like GPT, BERT, DALL-E), and implementing Retrieval-Augmented Generation (RAG) techniques. You will work closely with cross-functional teams to analyze large-scale datasets, build and adapt models, and ensure these models are deployed into production environments effectively. This role also includes AIOps responsibilities, where you will oversee model performance, retraining, and lifecycle management. 

Model Development & Optimization

• Build, fine-tune, and optimize a variety of AI/ML models including supervised, unsupervised, reinforcement learning, and generative models.
• Design models for specific use cases such as Natural Language Understanding (NLU), Dialogue Management, Knowledge Retrieval, Named Entity Recognition (NER), Intent Classification, Recommendation Systems, and Question-Answering (QA).
• Implement advanced Gen AI models for dynamic content generation, chatbots, and contextual understanding. 

AIOps and Model Lifecycle Management

• Develop automated pipelines for model training, testing, and deployment.
• Monitor and manage the health of AI models in production using AIOps techniques.
• Ensure continuous improvement and retraining of models based on performance metrics and evolving data trends.

Data Engineering & Integration

• Collaborate with Data Engineers to build data pipelines, perform ETL (Extract, Transform, Load), and preprocess large datasets.
• Implement data validation and entity resolution models for accurate information retrieval.
• Integrate AI models with external systems like SAP, ServiceNow, and other business-critical applications.

Cross-Functional Collaboration

• Partner with UI/UX Designers to integrate AI solutions into user-facing products.
• Work with Full Stack Developers to ensure seamless integration of AI models into both backend and frontend systems.
• Engage with QA Engineers to validate model robustness and accuracy through rigorous testing protocols.

AI Governance & Ethical AI

• Develop and enforce guidelines to ensure models are ethical, transparent, and free from biases.
• Implement data governance, model documentation, and compliance checks as part of the AI development lifecycle.
• Conduct periodic reviews to ensure alignment with responsible AI practices.

AI Research & Innovation

• Stay up-to-date with the latest advancements in AI/ML, including new generative AI technologies.
• Experiment with emerging models and frameworks to continually push the boundaries of AI solutions within the organization.
• Drive thought leadership through internal knowledge sharing, AI workshops, and external publications.

Required Skills

• 5+ years of experience in AI/ML engineering, data science, or a related field.
• Proven expertise in building models using frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Proficiency in Python, SQL, and experience with Azure (preferred), AWS, or Google Cloud for scalable AI/ML solutions.
• Strong understanding of Natural Language Processing (NLP), Computer Vision, Generative AI, and other advanced ML techniques.
• Experience with AI-driven solutions for dialogue management, NER, NLU, QA, OCR, and knowledge retrieval.
• Practical knowledge in integrating AI models with SAP, ServiceNow, or similar enterprise systems.
• Hands-on experience in using experiment tracking tools like Weights & Biases (W&B), and proficiency with AIOps tools and techniques.

Preferred Skills

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