N
AI Engineer
NxT Level
1 hour ago
Full-time
On-site
AI Engineer (GenAI Workflows for OTC Derivatives)
Location:
New York City (SoHo) — In-Person
Take the next step in your career now, scroll down to read the full role description and make your application.
About Our Client Our client is building the next-generation platform for institutional finance. The OTC derivatives market moves
trillions of dollars daily
on infrastructure that hasn’t fundamentally changed in decades—and this team is rebuilding that foundation with a
modern stack , a
delightful product experience , and an
AI-native architecture .
The founding team comes from trading, quantitative, and engineering roles at tier-one financial institutions and has built some of the most innovative products in the market. Their clients include sophisticated banks, hedge funds, and asset managers—meaning the problems are complex, the scale is massive, and the software will shape how this market operates for years to come.
Tech Stack Cloud-native, serverless-first architecture + Infrastructure-as-Code (IaC) Backend services in
Python
and
Java Frontend in
TypeScript / React Modern foundation models + AI tooling Automated SDLC using tools like
GitHub ,
Logfire ,
Vercel , and rapid intraday deployment practices
The Role
Our client is hiring an
AI Engineer
to build and productionize generative AI workflows that increase efficiency across the OTC derivatives lifecycle. You’ll own key parts of the
evaluation and context engineering
infrastructure—ensuring model outputs are high-quality, reliable, and explainable.
You’ll work closely with financial engineers and domain experts to design
retrieval, grounding, and validation
pipelines for complex derivatives data. The work includes building tools for knowledge extraction from trade documents, improving data consistency across the trade lifecycle, and integrating AI reasoning into workflows like settlement, valuation, and risk management.
What You’ll Do Build GenAI workflows that automate and enhance OTC derivatives processes end-to-end Own
context engineering
(retrieval, grounding, memory, and data shaping) to improve response quality and reliability Build evaluation systems (offline + online) to measure accuracy, consistency, and explainability Design structured output patterns (schemas, constrained generation, validation) for production-grade AI Create pipelines to extract and normalize data from unstructured trade documents (confirmations, term sheets, etc.) Integrate AI reasoning into downstream workflows (settlement, valuation, risk, cashflows, reconciliation) Collaborate with domain experts to translate financial requirements into robust AI systems Contribute to a high-velocity engineering culture with strong SDLC practices and rapid deployments
Requirements Bachelor’s degree in Computer Science, Engineering, Mathematics, Financial Engineering, or related field In-person
role in NYC (SoHo) Strong proficiency in
Python
(Java experience is a plus) Proficiency in prompt engineering and
structured output generation
(JSON, Pydantic schemas, validation) Experience with NoSQL databases (e.g.,
MongoDB ,
DynamoDB ) Familiarity with cloud infrastructure and deployment tools (CDK, Terraform) and cloud providers (AWS, GCP, Azure) Strong understanding of both traditional SDLC and AI SDLC (versioning, testing, rollout, monitoring) Strong software design fundamentals Excellent problem-solving and communication skills
Bonus Qualifications Experience fine-tuning or deploying LLMs (OpenAI, Anthropic, Gemini, etc.) Familiarity with vector databases and
RAG
pipelines Experience building AI agents or workflow orchestration for financial data tasks Understanding of OTC derivatives data models and trade representations (e.g., ISDA concepts) Experience integrating AI systems with pricing, settlements, risk, or cashflow infrastructure Familiarity with LangChain, PydanticAI, LlamaIndex, or similar frameworks Experience extracting data from unstructured financial documents (confirmations, term sheets, schedules) Strong grasp of evaluation, hallucination mitigation, and data quality management in production AI systems
Why This Role This is a rare chance to build
production-grade AI systems
in a domain where correctness matters and the upside is massive. xsgimln If you want to own foundational AI infrastructure, work alongside domain experts, and modernize a trillion-dollar market—this role is built for high-impact engineers.
New York City (SoHo) — In-Person
Take the next step in your career now, scroll down to read the full role description and make your application.
About Our Client Our client is building the next-generation platform for institutional finance. The OTC derivatives market moves
trillions of dollars daily
on infrastructure that hasn’t fundamentally changed in decades—and this team is rebuilding that foundation with a
modern stack , a
delightful product experience , and an
AI-native architecture .
The founding team comes from trading, quantitative, and engineering roles at tier-one financial institutions and has built some of the most innovative products in the market. Their clients include sophisticated banks, hedge funds, and asset managers—meaning the problems are complex, the scale is massive, and the software will shape how this market operates for years to come.
Tech Stack Cloud-native, serverless-first architecture + Infrastructure-as-Code (IaC) Backend services in
Python
and
Java Frontend in
TypeScript / React Modern foundation models + AI tooling Automated SDLC using tools like
GitHub ,
Logfire ,
Vercel , and rapid intraday deployment practices
The Role
Our client is hiring an
AI Engineer
to build and productionize generative AI workflows that increase efficiency across the OTC derivatives lifecycle. You’ll own key parts of the
evaluation and context engineering
infrastructure—ensuring model outputs are high-quality, reliable, and explainable.
You’ll work closely with financial engineers and domain experts to design
retrieval, grounding, and validation
pipelines for complex derivatives data. The work includes building tools for knowledge extraction from trade documents, improving data consistency across the trade lifecycle, and integrating AI reasoning into workflows like settlement, valuation, and risk management.
What You’ll Do Build GenAI workflows that automate and enhance OTC derivatives processes end-to-end Own
context engineering
(retrieval, grounding, memory, and data shaping) to improve response quality and reliability Build evaluation systems (offline + online) to measure accuracy, consistency, and explainability Design structured output patterns (schemas, constrained generation, validation) for production-grade AI Create pipelines to extract and normalize data from unstructured trade documents (confirmations, term sheets, etc.) Integrate AI reasoning into downstream workflows (settlement, valuation, risk, cashflows, reconciliation) Collaborate with domain experts to translate financial requirements into robust AI systems Contribute to a high-velocity engineering culture with strong SDLC practices and rapid deployments
Requirements Bachelor’s degree in Computer Science, Engineering, Mathematics, Financial Engineering, or related field In-person
role in NYC (SoHo) Strong proficiency in
Python
(Java experience is a plus) Proficiency in prompt engineering and
structured output generation
(JSON, Pydantic schemas, validation) Experience with NoSQL databases (e.g.,
MongoDB ,
DynamoDB ) Familiarity with cloud infrastructure and deployment tools (CDK, Terraform) and cloud providers (AWS, GCP, Azure) Strong understanding of both traditional SDLC and AI SDLC (versioning, testing, rollout, monitoring) Strong software design fundamentals Excellent problem-solving and communication skills
Bonus Qualifications Experience fine-tuning or deploying LLMs (OpenAI, Anthropic, Gemini, etc.) Familiarity with vector databases and
RAG
pipelines Experience building AI agents or workflow orchestration for financial data tasks Understanding of OTC derivatives data models and trade representations (e.g., ISDA concepts) Experience integrating AI systems with pricing, settlements, risk, or cashflow infrastructure Familiarity with LangChain, PydanticAI, LlamaIndex, or similar frameworks Experience extracting data from unstructured financial documents (confirmations, term sheets, schedules) Strong grasp of evaluation, hallucination mitigation, and data quality management in production AI systems
Why This Role This is a rare chance to build
production-grade AI systems
in a domain where correctness matters and the upside is massive. xsgimln If you want to own foundational AI infrastructure, work alongside domain experts, and modernize a trillion-dollar market—this role is built for high-impact engineers.