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Generalist software engineer title, metro locations, and broad AI/backend requirements increase applicant density.
Core software and LLM-integration skills transfer across industries, with moderate specialization from enterprise risk controls.
Technical filters like Python, APIs, LLM integration and cloud create moderate shortlisting rigidity.
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Engineer and deploy AI-enabled software, services, and automation that enhance risk controls and governance processes.
Integrate LLM capabilities, APIs, and workflow orchestration for scalable, secure backend and lightweight web solutions.
Collaborate with engineers and stakeholders to convert ambiguous requirements into clear technical solutions aligned to control strategies.
Proficient in Python (backend/AI integration), JavaScript/TypeScript (web), SQL, REST and GraphQL APIs, and software engineering fundamentals (CI/CD, debugging, Git).
Experience with LLM integration, retrieval-augmented generation, embeddings, vector databases, and event-driven architectures (queues, webhooks, pub/sub).
Familiarity with secure, scalable system practices including OAuth, API keys, service accounts, and cloud/enterprise platforms.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in building and maintaining AI-integrated software solutions in environments requiring scalable, secure backend and frontend components.
Comfortable working within multidisciplinary teams bridging engineering and business, translating complex needs into technical deliverables.
Knowledgeable or aware of risk and control frameworks or enterprise risk management to align technical implementations with governance needs.