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# AI Engineer

- **Company:** NxT Level
- **Location:** New York, NY, United States
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Data Retrieval, Amazon DynamoDB, Github, Information Extraction, JSON, Python (Programming Language), MongoDB, NoSQL, Performance Tuning, Systems Development Life Cycle, OpenAI, Next.js, Software Engineering, TypeScript, Management of Software Versions, Google Cloud, LangChain, ReactJS, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Hallucination Detection, Llamaindex, Backend, Agentic-AI, Information Technology, Deployment Automation, Structured Output Generation, PydanticAI Framework, Front End Software Development, Google Gemini, Terraform
- **Published:** October 14, 2026
- **Apply:** https://www.thejobnetwork.com/job/828871e6-7a5d-4d81-8416-238a9f6c7508/ai-engineer

## About the Role

* 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

## Description

* 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, 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