Senior Infrastructure Developer

As a Software Engineer, you will design, implement, and operate multi-agent workflows that bring automation and intelligence to accelerate asset management.

What you get to do in this role: Design and build intelligent agent workflows that coordinate specialized AI agents through controlled orchestration frameworks.

Engineer end-to-end multi-agent pipelines — from data retrieval and reasoning to evaluation and decision-making.

Integrate frontier AI models (OpenAI, Gemini, Claude), or fine-tuned internal models for reasoning, enrichment, and contextual decision-making — select and switch models based on complexity, cost, and accuracy trade-offs.

Design, develop and maintain existing features, and enhance platform capabilities based on user feedback.Develop robust back-end services and APIs (Python or ServiceNow).

Collaborate closely with SAM/HAM domain experts,product managers, and engineers to translate automation goals into resilient agentic systems.

Build intuitive UIs and dashboards in Service Portal, React, or Angular Leverage retrieval-augmented generation (RAG) strategies to ground AI reasoning in factual context from data sources — ensuring decisions are traceable and verifiable.

Apply observability, logging, and traceability across all agent workflows to ensure reproducibility, reliability, and continuous improvement.

Participate in code reviews, post-deployment analysis, and production debugging to maintain system quality and stability at scale.

To be successful in this role you have: Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving.

This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. 6-8 years of software development experience with strong command on a any object oriented language Java/Python.

Hands-on experience developing LLM-powered or agent-based systems using frameworks for production use cases.

Deep understanding of REST APIs, event-driven systems, asynchronous workflows, and scalable back-end design.

Experience implementing frontier AI models in enterprise workflow applications.

Strong problem solving & foundation in data structures, algorithms, object-oriented design, and design patterns, with attention to performance and scalability.

Experience deploying AI or automation systems in production, emphasizing versioning, traceability, and performance monitoring.

Familiarity with retrieval-augmented generation (RAG), vector stores, and prompt optimization.

Working knowledge of the ServiceNow platform or willingness to learn it.

Strong focus on code quality, observability, logging, and traceability — these are non-negotiable.

Demonstrated ability to leverage or critically evaluate AI integration in work processes, decision-making, or problem-solving — including using AI-powered coding tools, automating workflows, analyzing AI-driven insights, and exploring AI's impact on business functions.