Software Engineer (Agentic AI Applications)

About the role

You will help build and scale a newly launched AI-first product with direct revenue impact. Our platform analyzes client communications and engagement signals to help mortgage bankers focus on the clients most likely to convert. You'll build agentic AI applications: systems where agents reason, plan, use tools, and act on their own to drive real business outcomes. This role is for an engineer who wants to ship real product: building features across the stack, deploying to production, and learning from real usage. AI is not a side tool here. You'll use it as a core part of how you design, code, test, debug, review, and ship software. You'll work closely with engineers, product partners, and business stakeholders across distributed teams.

Responsibilities

• Build and ship full-stack features from design through implementation, release, measurement, and iteration.
• Contribute to agentic AI capabilities such as agent orchestration, tool calling, retrieval-augmented workflows, and LLM-powered features.
• Help build evaluation, observability, and guardrail mechanisms for AI agents, ensuring reliability, safety, and measurable quality as agent behavior translates into product functionality.
• Implement scalable backend and frontend solutions using technologies such as C#, Python, Angular, AWS, and Kubernetes.
• Work with product, design, data, and senior engineers to turn requirements into reliable, well-tested solutions.
• Participate in code reviews, design discussions, and team practices that raise the quality bar.
• Collaborate effectively across distributed engineering teams, including partners in India.

Requirements

• 2+ years of professional software development experience.
• Experience building production software in languages such as C#, Java, Python, JavaScript, or TypeScript.
• Experience contributing to full-stack applications across frontend, backend, APIs, or data integrations.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Solid grasp of software design fundamentals, testing, and writing maintainable code.
• Ability to communicate clearly and collaborate effectively across teams.

Preferred Qualifications

• Hands-on experience with LLM APIs, agentic AI frameworks (e.g., LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar), or Model Context Protocol (MCP) tool integrations.
• Exposure to LLM application patterns such as retrieval-augmented generation (RAG), prompt engineering, or evaluating AI output quality.
• Hands-on usage of AI tools in your development workflow, such as AI coding agents, AI-assisted reviews, or prompt-driven development.
• Experience with AWS, Kubernetes, CI/CD, or automated testing.