IT Software Engineer

About the Role

As a Software Engineer for AI in IT, you will design, build, and deploy autonomous AI agents, tool-calling systems, and intelligent workflows that automate and transform enterprise operations. You will turn complex business requirements into reliable, secure, and production-grade Agentic AI systems leveraging modern LLM orchestration and AI-first engineering practices.

Key Responsibilities

• Build, test, and deploy autonomous AI agents and multi-agent workflows (e.g., using frameworks like LangChain, LangGraph, AutoGen, or CrewAI) to automate enterprise processes.
• Integrate LLMs with internal enterprise databases, APIs, and tools via standard protocols (e.g., Model Context Protocol - MCP, OpenAPI, Function Calling).
• Apply AI-first development techniques, including advanced prompt engineering, RAG (Retrieval-Augmented Generation), agentic memory management, and structured output parsing.
• Develop maintainable, high-quality code in Python/TypeScript using AI-augmented coding environments (e.g., Cursor, Cody, Claude) while ensuring outputs pass strict security and compliance standards.
• Implement evaluation frameworks (Evals) and observability metrics to monitor agent accuracy, hallucination rates, latency, and tool-execution reliability in production.
• Debug and resolve agent loop failures, non-deterministic behaviors, and tool-integration errors using structured troubleshooting and diagnostic tools.
• Contribute to automated CI/CD pipelines, unit testing for non-deterministic AI outputs, and secure guardrail mechanisms (e.g., input sanitization, rate limiting, human-in-the-loop approvals).

Requirements & Tech Stack

• Hands-on software engineering experience with strong proficiency in Python or JavaScript/TypeScript.
• Practical experience building applications with Large Language Models (LLMs), including function calling, structured outputs, and Retrieval-Augmented Generation (RAG).
• Hands-on familiarity with agentic frameworks/patterns (e.g., LangGraph, LangChain, AutoGen, CrewAI, LlamaIndex) or building custom agent loops.
• Solid understanding of the Model Context Protocol (MCP), tool integration via REST APIs/GraphQL, and prompt engineering best practices.
• Experience using AI developer tools (e.g., Cursor, Claude, Cody) with an enthusiastic mindset toward experimental "vibe coding" and agentic workflows.
• Strong understanding of SDLC, Git, containerization (Docker), and foundational DevOps/DevSecOps practices.
• Strong problem-solving abilities and quantitative analytical skills; comfortable dealing with non-deterministic systems and iterative debugging.
• Excellent communication skills and a collaborative, action-oriented mindset.

Must-Have Qualifications

• Demonstrated experience connecting AI models/agents with 3rd-party enterprise platforms or databases (e.g., Salesforce, ServiceNow, SAP, Workday, Vector DBs).
• Experience implementing AI evaluation (Evals), benchmarking, and observability tools (e.g., LangSmith, Phoenix, Arize, Weights & Biases, or similar).
• Basic familiarity with cloud infrastructure (GCP, AWS, or Azure) for deploying containerized AI workloads.