1–5 years of experience in software engineering and production-quality Agentic AI Systems.
Experience of generating code using prompts and navigating fluidly through generated code using GitHub Copilot or OpenAI Codex or similar platforms
Design, build, evaluate, deploy, and maintain AI-enabled and agentic applications for enterprise use cases.
Develop backend services and orchestration layers using Python and frameworks such as FastAPI, Flask, or Django; TypeScript/Node.js experience is also valuable.
Collaborate with front-end teams using React, Next.js, Angular, or Vue.js to deliver high-quality user experiences.
Build AI application workflows that use LLMs, retrieval and grounding, tools, structured outputs, and APIs to complete business tasks reliably.
Build and integrate REST and/or WebSocket APIs and connect applications to internal and external tools, services, and enterprise data sources.
Deploy and operate applications on at least one cloud platform such as Azure, AWS, or GCP/Vertex AI.
Create and maintain evaluations for AI features and agent workflows, including quality checks, regression testing, and task-success criteria.
Participate in code reviews, testing, documentation, and continuous improvement of engineering workflows.
What You Will Need
Strong software engineering fundamentals and hands-on experience building production-grade applications, services, APIs, or microservices.
Proficiency in one or more backend languages such as Python, Java, or TypeScript/Node.js.
Experience with modern front-end frameworks such as React, Angular, Vue.js, or Next.js, or strong collaboration experience with UI engineers.
Experience designing and consuming REST and/or WebSocket APIs and integrating with databases, services, and external systems.
Hands-on experience with at least one major cloud platform such as Azure, AWS, or GCP/Vertex AI.
Interest in or hands-on experience building production AI systems, including LLM-powered features, agentic workflows, or intelligent automation use cases.
Experience applying software engineering discipline to AI systems, including testing, evaluation, iteration, and production readiness.
Experience using Git and standard engineering practices such as branching, code reviews, issue tracking, and CI/CD.
Experience working in Agile teams and managing delivery through tools such as Jira.
Ability to learn quickly and apply strong engineering judgment while working across evolving AI technologies and application requirements.
What Would Be Nice to Have
Experience building AI applications with LLM integrations, retrieval or grounding systems, vector search, tool or function calling, or agent frameworks.
Experience evaluating AI applications and agent workflows using test datasets, automated checks, grader-based evaluations, and regression testing.
Experience with observability and tracing for distributed or AI systems, including logs, metrics, traces, and production debugging.
Experience with security controls for AI applications, including access control, secrets handling, guardrails, prompt-injection awareness, and human-in-the-loop approvals where appropriate.
Experience with CI/CD, automated deployments, GitHub Actions, or similar engineering delivery pipelines.
Experience in rapid prototyping and iterative product development.
Experience building low-code solutions using Power Apps, Copilot Studio, or similar platforms.
Guidehouse is a leading global consulting firm that helps clients transform complex challenges in strategy, technology, operations, and risk into sustainable solutions. The company holds a strong position in management, digital, and public-sector consulting, serving commercial and government clients across multiple industries. Guidehouse’s strengths include deep domain expertise, technology‑enabled transformation, and a focus on innovation, data‑driven insights, and AI‑powered services.
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