Quality Assurance Engineer - Agentic AI

AkzoNobel; Pune District; On-site; INR 1,500,000 - 3,500,000; Full time

Job summary

AkzoNobel in Pune is seeking a Quality Assurance Engineer - Agentic AI to design, develop, deploy, and optimize enterprise-grade Agentic AI and automation solutions using Azure AI Foundry, Azure OpenAI, and modern APIs.

You will work across teams to ensure secure, observable, and reliable AI ecosystems, troubleshoot hallucinations and integration issues, and support Agile/DevOps delivery to accelerate AI transformation initiatives.

Qualifications

• 3–8 years of professional experience
• Strong Python development and enterprise integration expertise
• Experience with Azure AI Foundry, Azure OpenAI, Azure AI Agent Service, Semantic Kernel, RAG architectures, vector databases and cloud-native apps
• Experience with CI/CD, MLOps, monitoring, observability, testing, DevOps and Agile practices
• Bachelor or Master in CS/IT/AI/SE/Data Science or related field
• Roles include progression from support to independent leadership and governance in AI initiatives

Responsibilities

• Translate business requirements into AI designs, user stories, and implementation plans for agentic AI initiatives
• Lead end-to-end delivery using Azure-native AI services, APIs, and modern tech stacks
• Ensure secure, observable, and reliable AI ecosystems with proper governance and documentation
• Troubleshoot hallucinations, orchestration failures, and model performance issues
• Collaborate in Agile/DevOps environments and support planning, estimation, and deployment readiness

Skills

Python

Azure AI Foundry

Azure OpenAI

Azure AI Agent Service

Agent Behavior Testing

Prompt Regression Testing

LLM Orchestration

Responsible AI

REST API Testing

RAG Pipelines

Education

Bachelor or Master in Computer Science / IT / AI / Software Engineering / Data Science

Tools

Semantic Kernel

Vector Databases

CI/CD Pipelines

Job description

Dear Candidate,

Greetings from AkzoNobel (Experts in the proud craft of making paints and coatings since 1792)

We are hiring for the role of Quality Assurance Engineer - Agentic AI at Pune (Hybrid role), below are additional details as required.

Skills Required

Python, Azure AI Foundry, Azure OpenAI, Azure AI Agent Service, Agent Behavior Testing & Trace Evaluation, Prompt Regression Testing & LLM Orchestration Review, Responsible AI & Safety Guardrails Validation, REST API Testing & Enterprise Integration Validation, RAG Pipeline Validation & Retrieval Accuracy Testing etc

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.

Job Requirements

.

• Professional experience ranging from 3-8 years
• 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.
• Bachelor or Master degree in Computer Science, Information Technology, Artificial Intelligence, Software Engineering, Data Science, or a related discipline
• Capability expectations differ across MM1MM3 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.

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.

Regards,

Team AkzoNobel