The Gen AI Architect will lead the design and implementation of advanced AI, Machine Learning, and Generative AI solutions. This role requires deep hands-on expertise across NLP, computer vision, recommender systems, and MLOps, along with strong architectural leadership to build scalable enterprise-grade AI solutions.
Key Responsibilities
- Architect and deliver end-to-end data-driven solutions using statistical techniques, machine learning, deep learning, and MLOps frameworks.
- Lead and guide teams in developing AI/ML models across supervised, unsupervised, and deep learning algorithms.
- Own the MLOps lifecycle including model validation, explainability, monitoring, retraining, and governance (AI Glass Box).
- Develop solutions across: NLP: Latent semantic analysis, document similarity, document classification, text analytics, language models.
- Recommender Systems: Content-based and collaborative filtering models.
- Computer Vision: Neural networks, CNNs, RNNs, and advanced image analytics.
- Perform statistical modeling and predictive analytics to support business insights.
- Conceptualize, design, and implement Generative AI (GenAI) solutions—mandatory skill.
- Engage with stakeholders to translate business problems into scalable AI architectures and roadmaps.
Experience Requirements
- 10+ years of experience in AI/ML model development and deployment, with strong expertise in deep‑dive analytics.
- 3+ years of experience as a Solution Architect.
Technical Skills
Programming & Frameworks: Python, Keras/TensorFlow, NLTK
Big Data & Distributed Computing: PySpark, Hive
Databases: SQL
Strong knowledge of machine learning pipeline automation and MLOps best practices.evOps practices, and agile delivery frameworks.
• Familiarity with banking domain systems, regulatory considerations, and risk frameworks.
• Strong communication and stakeholder management skills, with the ability to influence across tech and business teams.
