Description
We are looking for a Senior Engineer to join our Apply for Circle Card (AFCC) team. As a Senior Engineer, you will serve as a specialist in the engineering team that supports the product. You will help develop and gain insight into the application architecture. You will design and implement the architecture on your own and influence fellow engineers by proposing software designs, providing feedback on software designs and/or implementation.
Responsibilities:
- Leverage AI-assisted software development tools throughout the software development lifecycle
- Collaborate effectively with AI coding assistants and enterprise AI platforms to improve engineering productivity
- Design and build production AI-powered applications using Target-approved AI platforms, frameworks, and Large Language Models (LLMs)
- Develop domain-specific AI agents and multi-agent workflows using modern orchestration frameworks
- Optimize AI applications for response quality, latency, throughput, token utilization, and inference cost
- Implement AI evaluation frameworks and apply responsible AI, security, privacy, governance, and observability best practices
- Build reusable AI components, frameworks, libraries, workflows, and engineering accelerators that improve developer productivity
- Design, develop, and maintain highly scalable AI-ready services using Kotlin, Java, and Micronaut
- Build secure, high-performance RESTful APIs and event-driven services supporting high-volume retail and financial workloads
- Design distributed systems using Kafka, asynchronous messaging, distributed caching, and cloud-native architecture patterns
- Design and optimize PostgreSQL databases, including data modeling, indexing, query optimization, and transactional integrity
Requirements:
- 4 years of degree or equivalent practical experience
- 5+ years of software engineering experience building enterprise-scale applications and backend systems
- Hands-on experience designing and deploying production AI applications, AI-powered tools, or agentic systems using modern Large Language Models (LLMs)
- Experience building autonomous or multi-agent workflows using technologies such as Model Context Protocol (MCP), LangGraph, LangChain, CrewAI, Google ADK, Semantic Kernel, or equivalent orchestration frameworks
- Strong understanding of AI application architecture, including Prompt Engineering, Context Engineering, Retrieval-Augmented Generation (RAG), tool calling, memory management, orchestration patterns, and AI evaluation
- Proficiency in Python for AI application development, automation, or AI tooling
- Expert-level proficiency in Kotlin and Java, with experience building microservices using Micronaut and/or Spring Boot