Generative AI Engineer

Job Description

Job Title: Senior AI Engineer - Core Platform

\n

Industry: FinTech

\n

Contract Duration: 6 months (rolling) 3 days on site

\n

Location: New York, NY (Hybrid)

\n

Hourly Rate: $65 - $85 per hour

\n

\n

The Role:

\n

\n
• You'll be building the AI infrastructure that others build on, designing and shipping scalable agent frameworks and multi-agent systems that power intelligent automation across a regulated fintech environment
\n
• Working alongside platform engineers, architects, and product teams, you'll create the reusable SDKs and APIs that let engineering and business teams move fast without sacrificing reliability
\n
• LLM integration will be a core part of your day to day, covering orchestration, routing, structured output, and standardising protocols like MCP and A2A across the platform
\n
• You'll take ownership of observability and tracing for agent workflows, making sure there's full visibility into how AI systems are behaving in production
\n
• Safety guardrails are not an afterthought here, you'll be embedding moderation, PII filtering, and cost controls directly into the platform from the ground up
\n
• You'll contribute to architectural strategy and evaluate systems for production readiness, bringing a strong opinion on scalability and operational tradeoffs
\n
• Documentation is part of the craft in this role, you'll produce clear, well-structured guidance on system architecture, SDK usage, and engineering standards
\n
• Collaboration is key, you'll partner closely with cross-functional teams to define best practices and raise the bar on how AI is built and deployed across the organisation
\n

\n

\n

Skills Needed:

\n

\n
• Solid Python engineering skills with hands-on experience building production AI and agentic systems, TypeScript or Java exposure is a bonus
\n
• Practical experience with agent orchestration frameworks, particularly AWS Strands SDK, and a strong grasp of multi-agent system design including async Python patterns
\n
• Proven ability to integrate LLMs into real products, including prompt engineering, structured output design, and context management
\n
• A platform engineering mindset, you know how to build developer-friendly SDKs and APIs that others actually enjoy using
\n
• Experience implementing observability and tracing for AI systems, with tooling such as OpenTelemetry or Langfuse being a plus
\n
• Familiarity with AI safety concepts including guardrails, moderation, and cost controls in a production setting
\n
• Strong written and verbal communication skills, with an ownership mentality and a focus on code quality and maintainability
\n
• Exposure to fintech or other regulated industries is advantageous but not essential
\n

\n