Job Description & Details
This role puts you right at the bleeding edge of agentic AI and LLM orchestration within enterprise infrastructure. If you enjoy building scalable, production-grade applications that leverage modern AI tools rather than just playing with wrapper scripts, this 12-month contract-to-hire position gives you a real platform to ship high-impact features. You'll be bridging the gap between traditional backend engineering and cutting-edge generative AI capabilities across multiple major financial hubs.
What You'll Actually Be Doing
You'll spend your days designing and building robust APIs, integrating complex LLM workflows, and ensuring that AI-driven features don't fall over under heavy enterprise loads. Expect to write clean Python or Java services, optimize SQL queries for heavy data ingestion, and stitch together distributed systems. The real challenge here is moving past prototype phase to deliver deterministic, low-latency, production-ready AI agents that business units can actually rely on daily.
The Core Tech Stack
You need a rock-solid foundation in Python and/or Java coupled with deep backend chops including SQL and API development. On the AI side, the team relies heavily on AWS Bedrock, Snowflake Cortex, and LangChain running on infrastructure like EC2. Familiarity with modern LLMs and coding assistants like Claude and ChatGPT is a given, but your ability to orchestrate these tools using LangChain within a cloud environment is what will ultimately make or break your day-to-day success.
Interview Expectations
Expect the technical interview to heavily probe your experience with distributed systems design and failure handling when using LLMs, such as how you manage token rate limits, context windows, and fallback logic when an external model API drops. The hiring manager is secretly looking for maturity in your architecture decisions—they want to know that you understand how to build resilient applications where third-party AI models are treated as inherently flaky dependencies rather than infallible black boxes.
Application Advice
Skip the generic summaries and make sure your resume immediately highlights your production-level Python or Java experience paired with tangible examples of deployed GenAI applications. Ensure keywords like AWS Bedrock, Snowflake Cortex, LangChain, and API development are front and center to clear the initial ATS filters, but back them up with bullet points that emphasize scale, latency reduction, and actual business outcomes rather than just listing tools.