Vacancies Functional Engineer AI

Job Purpose

The IT Engineer – Agentic AI Engineer is responsible for designing, developing, deploying, and continuously optimizing enterprise-grade Agentic AI and intelligent automation solutions across global business environments. The role combines the responsibilities of Functional Engineer AI and Technical Engineer AI to translate business requirements into scalable AI-driven products and services using Azure AI platforms, large language models, orchestration frameworks, and modern application technologies. The position focuses on implementing secure, observable, and reliable AI ecosystems leveraging Azure AI Foundry, Azure OpenAI, Azure AI Agent Service, Semantic Kernel, Retrieval-Augmented Generation (RAG) pipelines, vector databases, APIs, and enterprise integrations. The role also supports React.js and modern user interface development for AI-enabled applications, establishes AI evaluation criteria and observability standards, and ensures effective human-in-the-loop controls. Responsibilities include troubleshooting hallucinations, integration failures, and model performance issues while supporting Agile and DevOps delivery practices to accelerate enterprise-scale AI transformation initiatives.

Key Activities

Story specification: Translate business objectives, process requirements, and stakeholder expectations into detailed functional and technical designs, user stories, acceptance criteria, and implementation plans for agentic AI and intelligent automation initiatives. Collaborate with business teams, architects, and delivery stakeholders to align AI capabilities with enterprise priorities.  Development & delivery: Lead and support the end-to-end delivery of enterprise AI initiatives using Azure-native AI services, orchestration frameworks, APIs, RAG architectures, and modern application technologies. Ensure compliance with enterprise architecture, security, governance, documentation, and coding standards. Develop and review AI workflows, reasoning agents, integrations, React.js user interfaces, and automation components while enforcing testing and deployment conventions. Contribute to solution approvals and production deployment readiness.  Testing: Define and execute testing strategies for autonomous systems, reasoning loops, AI safety guardrails, and enterprise integrations. Support functional testing, business acceptance testing, and validation of observability, traceability, and model performance metrics. Verify production readiness and ensure AI solutions meet operational, security, and quality expectations.  Incident management: Monitor production AI environments for performance, security, reliability, and compliance issues. Conduct root cause analysis for hallucinations, orchestration failures, integration incidents, and degraded model behavior. Coordinate corrective actions, deploy fixes, improve dashboards and monitoring frameworks, and identify opportunities for automation and process optimization.  Project support: Own and contribute to project deliverables in Agile, DevOps, or waterfall delivery models. Collaborate with Project Managers, Scrum Masters, business stakeholders, and engineering teams to ensure timely and high-quality delivery of AI solutions. Support planning, estimation, documentation, stakeholder communication, and continuous improvement activities.  

Experience

• Bachelor or Master degree in Computer Science, Information Technology, Artificial Intelligence, Software Engineering, Data Science, or a related discipline. 


• Professional experience ranges from 0–1 years for entry-level engineering profiles through 6+ years for advanced engineering and leadership-oriented levels depending on MM1–MM3 maturity classification. 


• Strong technical expertise in Python development, AI application engineering, and enterprise integration technologies. Working knowledge of React.js, Node.js, REST APIs, orchestration frameworks, Azure AI Foundry, Azure OpenAI, Azure AI Agent Service, Semantic Kernel, Retrieval-Augmented Generation architectures, vector databases, and cloud-native application development. 


• Experience with CI/CD pipelines, MLOps practices, model monitoring, observability tooling, automated testing, DevOps delivery, and Agile ways of working. Ability to evaluate AI model quality, implement human-in-the-loop controls, troubleshoot hallucinations and reasoning failures, and optimize enterprise AI solutions for performance, security, and scalability. 


• Capability expectations differ across MM1–MM3 levels and include progression from supporting development activities and issue resolution to independently leading complex AI engineering initiatives, mentoring engineers, contributing to governance and safety standards, and driving enterprise AI transformation outcomes.