Technical Stack & Requirements:

• AI/ML Stack: Amazon SageMaker, AWS Bedrock.
• Design Focus: Vector databases, knowledge graphs, and semantic layers.
• Databricks: Experience with Databricks and Databricks Genie is highly desirable.
• Core Mandate: Define the standards for "AI-ready" data curation and direct the integration of AI methodologies into existing Service Experience and Financials business logic.

Qualifications:

• 6-8+ years of experience.
• Strong background in AI/ML design and implementation.
• Ability to architect semantic layers and knowledge graphs.
• Experience with GitHub Actions for managing codebases.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• 8+ years of software engineering experience.
• 3+ years of hands-on AI/ML engineering experience.
• Advanced Python programming skills.
• Strong experience building production-grade AI applications.
• Experience with Generative AI and Large Language Models.
• Experience designing Retrieval-Augmented Generation (RAG) architectures.
• Knowledge of vector databases and embedding models.
• Experience deploying AI solutions on AWS cloud platforms.
• Experience with CI/CD pipelines and DevOps practices.
• Strong understanding of distributed systems and scalable backend architectures.
• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Experience with Agentic AI frameworks (LangGraph, AutoGen, CrewAI, Semantic Kernel).
• Experience with AWS Bedrock, Amazon SageMaker, Azure OpenAI, or Vertex AI.
• Experience implementing Responsible AI frameworks.
• Financial services, fintech, or investment management domain experience.
• Experience building recommendation engines, intelligent assistants, or AI copilots.
• Knowledge of Kubernetes, Docker, and cloud-native microservices.
• AWS AI/ML certifications or equivalent.