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Job description
Senior Software Engineer - Build Production-Grade AI Agents
New York, NY
Own the AI agents that make engineers, quants, and traders faster
You'll design and ship real, production AI agents that cut through operational overhead and give developers, quants, and traders their time back.
This is not a "labs" role. You're building autonomous and semi-autonomous systems that touch codebases, APIs, internal tools, and data pipelines- safely, securely, and at scale.
You own architecture. You own implementation. You own the behavior of agents that must actually work in the wild.
If you've been itching to push beyond toy demos and build serious agentic systems in production, we want to talk to you.
What You'll Work On
You'll sit at the intersection of AI systems engineering and workflow automation, turning messy real-world processes into dependable AI-powered workflows:
Autonomous AI agents executing multi-step workflows across internal systems
Retrieval-augmented generation (RAG) architectures using structured, permissioned data
Agent memory and orchestration layers
Inter-agent communication patterns
Tool schemas that drive deterministic agent behavior
Safeguards for LLM failure modes and constraints
Automation that supports developers, quants, and traders
Evaluation and integration of new AI models and frameworks
You'll be working with Python at an expert level, RAG patterns and vector databases (e.g., Pinecone, Chroma, pgvector), and agent frameworks such as LangChain or LangGraph.
Your job: turn ambiguous workflows into robust agent systems that run with minimal human babysitting.
What You'll Be Doing
Designing and implementing autonomous AI agents that execute multi-step workflows across internal systems
Building and productionizing RAG architectures on top of structured, permissioned data sources
Developing durable agent memory and orchestration layers for complex workflows
Creating inter-agent communication patterns so multiple agents can coordinate effectively
Defining tool schemas and translating ambiguous stakeholder needs into deterministic agent behaviors
Implementing safeguards against hallucination, prompt drift, context issues, and rate limits
Partnering directly with developers, quants, and traders to uncover high-value automation opportunities
Deploying AI agents into production environments and iterating based on real-world feedback
Evaluating and integrating emerging models, frameworks, and open-source AI tooling into existing systems
Designing systems to operate reliably with minimal human intervention
Ensuring sensitive and proprietary data is handled and integrated securely within AI workflows
What We're Looking For
5-10+ years of software engineering experience
Proven track record building AI-powered applications, automation platforms, or agentic systems in production
Demonstrated ability to design systems that operate with minimal human intervention
Expert-level Python, including async, typing, packaging, and testing best practices
Experience building AI agents that interact with APIs, tools, codebases, and shell environments
Strong understanding of RAG patterns and vector databases such as Pinecone, Chroma, or pgvector
Familiarity with agent frameworks like LangChain, LangGraph, or similar tools
Deep understanding of LLM behavior, constraints, and mitigation strategies
Strong system design skills and comfort working independently in a fast-evolving space
Experience handling sensitive or proprietary data securely
The Experience That Will Really Get Our Attention
You've moved beyond chatbot front-ends and have actually wired LLMs into serious back-end systems- agents that call tools, inspect code, touch production-like data, and keep running without constant human guardrails.
You're comfortable owning an ambiguous problem end to end: mapping workflows, designing agent behaviors, choosing the right RAG and vector patterns, implementing safeguards, and hardening everything for production.
Why This Opportunity?
You'll be part of a newly formed initiative, so your architecture and design decisions won’t just be tickets—they’ll be the foundation for how AI agents are built going forward.
You're close to the users: developers, quants, and traders who will feel the impact of the systems you build every day.
If you want to own meaningful, production-grade AI systems instead of building yet another demo, this is the role.
senior software engineer, software engineer, AI engineer, AI systems engineer, machine learning engineer, agentic systems, AI agents, autonomous agents, workflow automation, Python, async Python, LLM, large language models, RAG, retrieval-augmented generation, vector database, Pinecone, Chroma, pgvector, LangChain, LangGraph, orchestration layer, inter-agent communication, prompt engineering, hallucination mitigation, prompt drift, context management, rate limiting, secure data handling, production AI, trading technology, quant tools, developer productivity, internal tools
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Worksite address
new york, NY, 10261, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.