GEN AI architect

Job Role: GEN AI architect

Job Location: NYC, NY

Job type: Full timer

Job Description

In this role, you will lead the design, delivery, and adoption of enterprise grade GenAI data platforms, with a strong emphasis on market data. You will own and ship agentic GenAI data workflows, including retrieval augmented generation (RAG), tool calling, and orchestration, while embedding governance, controls, and quality from day one.
You will design and scale AI agent capabilities across Azure and AWS, leveraging Microsoft 365 Copilot and Copilot Studio, and integrating leading OpenAI and Anthropic large language models (LLMs) with privacy, security, and regulatory controls built in. The role has deep responsibility for market data platforms .

Responsibilities::

• Lead the design and delivery of GenAI data architectures, including agentic workflows, RAG pipelines, prompt orchestration, and tool calling frameworks.
  Own the strategy and integration of market data platforms, ensuring high quality ingestion, normalization, enrichment, and access for analytics and GenAI use cases.
  Design and oversee scalable GenAI and machine learning data pipelines, supporting structured and unstructured data across the full AI lifecycle.
  Establish and embed data quality, lineage, privacy, and compliance controls to ensure responsible AI use and regulatory alignment.
  Partner with business, product, engineering, and data science teams to identify high impact GenAI use cases and translate them into production grade solutions.
  Define best practices for model lifecycle management, evaluation metrics, MCP integration, and continuous improvement of GenAI systems.
  Act as a trusted senior advisor to Markets and Investment Banking stakeholders.
  Build, lead, and mentor high performing teams of data engineers and GenAI practitioners.

Preferred Skills:

• Experience designing and overseeing GenAI and ML data pipelines, integrating structured and unstructured data for model deployment and evaluation.
  Strong expertise in enterprise GenAI and LLM platforms, including prompt engineering pipelines, vector databases, and retrieval augmented generation (RAG).
  Proven ability to establish data governance, privacy, and compliance frameworks in regulated financial services environments.
  Experience partnering with business and engineering teams to deliver production grade GenAI solutions.
  Strong leadership skills, with experience setting standards and best practices across data, analytics, and AI teams