AI engineer with Agentic experience

Overview

In this role you will help design and build an AI-powered investment-insights platform. You’ll collaborate with business teams, analysts, and architects to translate requirements into robust backend solutions while improving efficiency and output quality. You’ll lead technical reviews, craft production-grade APIs, and integrate generative AI capabilities at scale. This is a hands-on role that combines software engineering with AI/ML thinking to empower client-ready insights.

Responsibilities

• Collaborate with business teams, analysts, and architects to understand requirements and identify system changes
• Work with development teams, designers, and content specialists to implement enhancements
• Develop analyses and present recommendations; contribute to design and code peer reviews and testing
• Identify opportunities for refactoring and process improvement; defend and justify recommendations
• Propose solutions to solve project issues and improve efficiency across the SDLC
• Communicate effectively with both business and technical audiences; produce adaptable design documents
• Demonstrate technical thought leadership and problem-solving with a focus on innovation

Key requirements

• 8+ years of professional software development experience
• Strong backend Python development with clean, maintainable, testable production code
• Production API design and delivery using FastAPI; including versioning and OpenAPI/Swagger documentation
• Cloud experience (AWS) including eventing, queues, serverless, logging/monitoring, secrets
• Git workflows and CI/CD pipelines
• Debugging and problem-solving with ownership across the SDLC
• Experience building agents; vector databases and RAG patterns; LLM integration with prompt design and cost/perf tuning
• Strong organization and communication skills
• Ability to communicate to diverse audiences (business and technical)
• Interpersonal skills; capability to defend recommendations with confidence
• FastAPI
• OpenAI/Azure OpenAI/Anthropic or open-source LLMs
• Vector databases and retrieval-augmented generation (RAG)

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