AI Engineer

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).