AI Enterprise Architect with banking domain experience

JOB DESCRIPTION:

AI Enterprise Architect:

Key Responsibilities:

• AI Architecture & Strategy:
• Design and implement enterprise-grade AI architectures for investment platforms, trading systems, and financial analytics.
• Define and lead AI strategy aligned with investment performance, risk management, and regulatory requirements.
• Establish standards for Generative AI, Machine Learning, and real-time inference within financial systems.
• AI Solution Development:
• Design, develop, and deploy AI models tailored to specific project requirements, including Cognitive AI techniques such as NLP, Computer Vision, Speech, and Conversational AI.
• Demonstrate strong understanding of Document AI development processes, models, and applications.
• Apply knowledge of Generative AI models and applications to relevant business use cases.
• Deliver and maintain production-grade, end-to-end machine learning solutions, including data preparation, experimentation, model training, serving, and updating.
• MLOps & Deployment:
• Utilize MLOps methodologies to streamline development, deployment, and monitoring of machine learning models.
• Collaborate with data scientists, ML engineers, and stakeholders to transition prototypes to production environments.
• Optimize and refine algorithms to improve performance, scalability, and reliability.
• Integrate AI solutions into major cloud platforms (AWS, Azure, Google Cloud Platform).
• Implement CI/CD pipelines using Azure DevOps or TFS.
• Technical Leadership & Collaboration:
• Provide technical leadership and mentorship to AI team members.
• Work closely with cross-functional teams to integrate AI solutions into products and services.
• Stay current with AI, LLMs, MLOps, and machine learning trends, techniques, and best practices.

Requirements:

• 15+ years of experience in AI/ML, including practical experience with MLOps, Text Analytics, and Generative AI.
• Strong proficiency in Python and Java, with experience in frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn.
• Hands-on experience with cloud platforms (AWS, Azure, Google Cloud Platform).
• Expertise in machine learning and neural network architectures (Ensemble Models, SVM, CNN, RNN, Transformers).
• Experience with NLP tools (NLTK, SpaCy, Gensim) and MLOps tools (Kubeflow, MLflow, Azure ML).
• Practical knowledge of workflow orchestration tools (e.g., Airflow).
• Experience with Microservices architecture and building scalable REST APIs (FastAPI, Flask, Django).
• Proficiency with SQL and NoSQL databases (MongoDB, Postgres, Neo4j).
• Excellent problem-solving skills, collaborative mindset, and strong communication skills for diverse teams.

Preferred Qualifications:

• Bachelor s, Master s, or PhD in Statistics, Mathematics, Computer Science, or related quantitative field.