Technical Engineer AI

AkzoNobel — Pune, Maharashtra

₹3–7 LPA (HireSetu estimate) · Entry level · Freshers eligible · Full-time · onsite

HireSetu listing reference 00397f — confirm requirements below, then apply on the employer site.

Quick answer: An enterprise AI engineering role in Pune building agentic and RAG systems on Azure, with a heavy production side: monitoring live agents, triaging hallucinations and orchestration failures, then fixing them. The experience line runs from 0-1 years to 6+, split across three maturity levels, so this one listing describes a fresher's job and a lead's job.

From AkzoNobel's job posting

Since 1792, we’ve been supplying the innovative paints and coatings that help to color people’s lives and protect what matters most. Our world class portfolio of brands – including Dulux, International, Sikkens and Interpon – is trusted by customers around the globe. We’re active in more than 150 countries and use our expertise to sustain and enhance the fabric of everyday life. Because we believe every surface is an opportunity. It’s what you’d expect from a pioneering and long-established paints company that’s dedicated to providing sustainable solutions and preserving the best of what we have today – while creating an even better tomorrow. Let’s paint the future together.

For more information please visit www.akzonobel.com

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. 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.

The IT Engineer – Agentic AI Engineer reports to an IT Team Lead or Domain Lead depending on the Agile or DevOps organizational structure. The position collaborates closely with External Consultants and Vendors, Project Managers and Scrum Masters, IT Business Partners, Enterprise and Solution Architects, Global Process Owners/Global Process Design Leads, Process Coaches, and cross-functional technology and business teams. The role has no direct or indirect reports but may coordinate contributors and delivery activities within enterprise AI programs and projects.

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. 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.

The employer’s company overview, benefits and equal-opportunity statement are left out here; they are on the original posting.

Text from AkzoNobel's official job posting, formatted by HireSetu. The employer's own page is the final word on details.

HireSetu's note

One posting, three levels

The experience band is the first thing to sort out. It runs from 0-1 years up to 6+, mapped to MM1 to MM3, which means a fresher supporting development and clearing issues, a mid-level engineer owning delivery end to end, and a senior who mentors and shapes governance and safety standards. Work out which one you are before applying and aim your CV at that level.

A fresher fit exists here, but it is conditional: a computer science, IT, AI or data science degree, solid Python, and a project where you actually built retrieval over your own documents with an LLM API. Coursework and a ChatGPT habit won't carry the conversation. React.js appears in the mix too, so a front-end side to your work helps, though nobody expects depth in every tool named.

The production half of the job

Deploying an agent is the midpoint. The listing puts real weight on what follows: monitoring production for performance, security and reliability, then running root cause analysis when a model hallucinates, an orchestration step fails or an integration breaks. Dashboards, traceability, evaluation criteria and human-in-the-loop controls are as much your job as building the workflows.

Revise the things that make agents behave: how retrieval quality shapes answers, what you would measure to separate a good response from a bad one, and how to follow a multi-step call through logs. Testing gets its own bracket, covering guardrails and acceptance testing alongside business teams.

AkzoNobel makes paints and coatings, so the processes behind these agents come from manufacturing, supply chain and commercial teams. You coordinate those stakeholders without managing anyone directly.

Drafted with AI from AkzoNobel's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.

How to apply

Open AkzoNobel's careers site with the Apply button.
Follow the application steps on the site and upload your resume.
Keep the confirmation email or application number for follow-up.

Apply on AkzoNobel's site

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