Applied AI Engineer

Exp: 14+ Years

Must Have:

• Strong background within Machine Learning

o Everything withing AI starts here

• Strong within Python (NOT looking for a software developer who has learned how to use models. Instead, looking for a core math background, machine learning, data model extraction and/or trading models.

o Pytorch

o Langchain

o Core AI Tools

• Document Extraction Experience and comfortable enhancing the model to produce better extraction
• RAG experience is a MUST
• Agentic Workflows Exp

o Ability to orchestrate workflows of a system. Someone who has applied their knowledge into apply agentc workflows - move through steps from beginning to end. Do transformation on it and produce a result

• Llm OPS
• NOT LOOKING FOR A SOFTWARE DEV WHO learned how to use models
• Building production systems and not just trying to learn something

o Someone who has applied knowledge into how EFFECTIVELY they can apply agentic workflows

o Must have professional experience working with a LIVE system, opposed to someone doing this recreationally

• Financial Services is a HUGE plus

Plus:

• Engineering Exp front to back

o Build workflows, examine systems, suggest solutions to it using data flows and gen AI, etc

Fixed Income or Institutional Lending domain experience.

• Experience working in regulated environments with strong audit and control requirements.
• Familiarity with enterprise security, data governance, and entitlement models.
• Experience designing reusable internal platforms or shared developer tooling.
• Frontend experience is beneficial (Angular or React)

D2D;

• Design and evolve reusable GenAI workflows used across Lending business lines.
• Develop an enterprise grade AI-based document ingestion and data extraction capability, including traceability, confidence scoring, and human-in-the-loop review.
• Build AI-powered assistants embedded in Lending systems using agentic workflows.
• Deliver automated content and deck generation workflows for reporting and approvals.
• Provide expert advice on GenAI architecture including model selection, orchestration patterns, and evaluation strategy.
• Establish LLMOps practices: extraction accuracy, assistant reliability, prompts management, and audit monitoring.
• Design and implement controls for entitlements, PII handling within open-source models in a regulated environment.
• In the role you are expected to act as a hands-on technical expert, and it has a clear path to becoming a platform owner responsible for shared GenAI standards across Lending.

Misc:

• They do not have this type of candidate on team today - they need this candidate to come in and be a SME on the team and be confident enough to bring their own two sense into the role.
• AI Platform is being built from scratch

o Need this candidate to bring knowledge of how to chain workflows and how to enhance extractions

• Stakeholders

o Strats (heavy math background) + Operations Users + Business Users

• Current Volume of Documents: Lending volume is Massive

o Docs can go from 1 page to 300+ pages (PDFs)

Redflags:

• Developer who simply is using AI

Ideal Candidate:

• Applied AI SME - how can come in and say " THIs is how it needs to be done, this is what I will need to do it, this is my timeline, etc."
• Practitioner is someone who not just someone giving out straight answer