New opportunity

Senior AI Engineer

Donaldson · Gurgaon, India

About this role

The AI Center of Excellence (AI CoE) at Donaldson is seeking a Senior AI Engineer to lead the design, development, and deployment of enterprise AI solutions that create measurable business impact across the organization. This senior individual contributor role involves driving AI initiatives from concept through production while working closely with Principal AI Engineers, enterprise architects, business stakeholders, and product teams.

Responsibilities

Lead the end-to-end delivery of AI and Generative AI solutions, taking ownership from business discovery and solution design through deployment and operational support.
Work directly with business stakeholders to identify opportunities, define requirements, prioritize use cases, and translate business challenges into practical AI solutions.
Design scalable solution architectures leveraging LLMs, RAG, AI agents, enterprise knowledge sources, APIs, workflow automation, and cloud services.
Develop and deploy production-grade AI applications, services, and copilots using Python, Azure AI, Azure OpenAI, Snowflake, and related enterprise technologies.
Drive technical decisions across AI projects, evaluating architecture options, implementation approaches, trade-offs, risks, and long-term maintainability.
Establish and implement testing, evaluation, and monitoring practices to ensure AI solutions are accurate, reliable, secure, and production ready.
Collaborate with architects, data engineers, platform teams, security teams, and business partners to successfully integrate AI capabilities into enterprise systems and processes.
Build reusable frameworks, accelerators, templates, and engineering standards that improve delivery speed and enable scalable AI adoption across the organization.
Provide technical leadership through architecture reviews, design guidance, code reviews, technical coaching, and sharing best practices across the AI CoE.
Continuously evaluate emerging AI technologies and recommend innovative solutions that deliver measurable business value and strategic advantage.

Required qualifications

Master’s or Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
4-6 years of relevant professional experience in AI, machine learning, data, or software engineering with min 1 year of hands-on experience designing, developing, and deploying AI, Machine Learning, or Generative AI solutions end-to-end.
Strong Python development skills and experience with software engineering principles including modular design, API development, testing, version control, secure coding practices, system integration, and maintainable application architecture.
Strong understanding of modern Gen AI architectures including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, evaluation frameworks, deterministic orchestration and AI solution architectures.
Hands-on experience with AI application and orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, LlamaIndex, or equivalent technologies.
Proven ability to operate in ambiguous environments, independently drive technical solutions, and lead initiatives from concept through production deployment.

Preferred qualifications

Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, Microsoft Copilot Studio, Snowflake Cortex AI, or other enterprise AI platforms.
Experience designing and developing enterprise AI applications, copilots, intelligent assistants, automation solutions, or agent-based systems deployed to business users.
Experience with CI/CD, DevOps, MLOps, and LLMOps practices for enterprise AI solution deployment and support.
Experience with containerization and cloud-native development technologies such as Docker, Kubernetes, Azure Container Apps, or Azure Kubernetes Service (AKS).
Experience building AI-enabled applications and services using frameworks such as FastAPI, Flask, Streamlit, React, Next.js, .NET, or equivalent application development frameworks.
Demonstrated experience leading the technical delivery of complex software, AI, or automation projects involving multiple stakeholders, systems, and business functions.
Familiarity with enterprise governance controls for GenAI (privacy, safety, auditability).
Experience optimizing inference costs/latency and implementing observability for AI features.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonAzure AIAzure OpenAISnowflakeLangChainLangGraphSemantic KernelAutoGenCrewAILlamaIndexAzure AI FoundryAzure AI SearchMicrosoft Copilot StudioSnowflake Cortex AIDockerKubernetesAzure Container AppsAzure Kubernetes Service (AKS)FastAPIFlaskStreamlitReactNext.js.NET

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End Delivery of Generative AI Solutions
Take full ownership of AI initiatives from initial business discovery and solution design through to production deployment, evaluation, and operational support within the AI Center of Excellence.
Scalable Architecture Design and Implementation
Design and build scalable solution architectures leveraging modern Gen AI components such as Large Language Models, Retrieval-Augmented Generation, and AI agents using Python and cloud services like Azure OpenAI.
Standardization and Reusable Frameworks
Build reusable frameworks, accelerators, templates, and engineering best practices to accelerate safe and scalable enterprise-wide AI adoption.
Stakeholder Collaboration and Technical Leadership
Work directly with business stakeholders, product teams, and enterprise architects to define requirements, prioritize use cases, and provide technical guidance through code and architecture reviews.

Likely challenges to think through

Navigating High Ambiguity in Enterprise AI Scoping
Translating vague business challenges from stakeholders into concrete, production-ready AI solution architectures requires balancing fast-paced innovation with strict enterprise governance and reliability constraints.
Optimizing Inference Latency and Operational Costs
Deploying complex agentic and LLM applications at scale introduces trade-offs regarding token costs, response latency, and system observability that must be actively managed.

Practice your answers

Technical practice

End-to-End Generative AI Architecture Design

What to coverDiscuss how you design a production-grade Retrieval-Augmented Generation (RAG) system from business discovery to deployment using Azure OpenAI and Python. Address trade-offs between chunking strategies, vector database selection, and retrieval latency versus accuracy.

Orchestrating Multi-Agent Workflows

What to coverExplain how you would implement deterministic orchestration using frameworks like LangGraph or AutoGen for enterprise copilots. Discuss how to handle state management, error recovery, and loop prevention in complex agentic workflows.

Optimizing Inference Cost and Latency

What to coverWhat strategies do you use to minimize token costs and inference latency when deploying LLMs at scale? Discuss caching mechanisms, model selection, prompt compression, and asynchronous processing patterns.

Evaluating GenAI Application Performance

What to coverDescribe how you establish testing and evaluation practices for non-deterministic AI outputs. Detail how you incorporate automated evaluation frameworks, user feedback loops, and guardrails to ensure accuracy and reliability.

Enterprise Data Integration with Snowflake and Azure

What to coverHow do you securely connect enterprise data sources, such as Snowflake and Azure AI Search, to LLM applications while maintaining strict governance and data privacy controls? Discuss secure coding practices and access boundaries.

Containerization and Cloud-Native Deployment

What to coverWalk through containerizing a Python-based AI microservice using Docker and deploying it on Azure Kubernetes Service (AKS) or Azure Container Apps. Explain how you manage environment variables, scaling triggers, and resource limits.

Implementing LLMOps and CI/CD Pipelines

What to coverHow do you structure CI/CD pipelines for AI applications that include both traditional code and prompt/model configuration changes? Address automated regression testing for prompts and version control of vector embeddings.

Enterprise Governance, Privacy, and Safety Controls

What to coverWhat techniques do you implement to prevent data leakage, prompt injection attacks, and toxic output generation in enterprise conversational assistants? Discuss content moderation layers and auditability requirements.

Building Reusable Frameworks and Accelerators

What to coverHow would you design reusable templates and accelerators within an AI Center of Excellence to speed up adoption across different business units? Emphasize modular design, maintainable application architecture, and code reuse.

Behavioural practice

Navigating Ambiguity in Enterprise AI Requirements

What to coverDescribe a situation where you had to scope and drive an AI project from vague business requirements. Explain how you clarified objectives with stakeholders, prioritized use cases, and delivered a working solution despite initial uncertainty.

Technical Leadership and Mentorship Across Teams

What to coverGive an example of how you provided technical leadership through architecture reviews or code coaching. Explain how you aligned disparate engineering and security teams around best practices for an AI deployment.

Managing Conflicting Stakeholder Priorities

What to coverTell about a time when business stakeholders demanded rapid feature delivery while platform and security teams insisted on strict governance controls. How did you balance speed with safety, and what was the resulting impact?

Evaluating and Pivotting Away from Emerging Tech Hype

What to coverDescribe a scenario where you evaluated a newly released AI framework or model that ultimately did not fit the business need. Explain how you assessed its limitations and communicated your recommendation to leadership.

Sharpen your knowledge

Topics to prepare

Generative AI Architectures and RAG
Revise the concepts of Large Language Models, Retrieval-Augmented Generation, and agentic workflows. Relevance: Directly required for designing scalable solution architectures leveraging enterprise knowledge sources and LLMs.
AI Solution Governance and Security
Study enterprise governance controls, privacy safeguards, auditability, and secure coding practices for GenAI. Relevance: Essential for ensuring production-ready, secure, and compliant AI deployments within the AI CoE.
MLOps and LLMOps Practices
Review lifecycle management, CI/CD pipelines, containerization, and monitoring for AI features. Relevance: Matches requirements for establishing testing, evaluation, and operational support practices for enterprise applications.

Skills to revise

Python Orchestration Frameworks
Practice building multi-agent systems and retrieval workflows using tools like LangChain, LangGraph, or Semantic Kernel. Relevance: Directly aligns with hands-on technical requirements for developing production-grade AI services and copilots.

Your preparation checklist

Build and document an end-to-end RAG or AI agent project
Completion looks like a working Python application using LangChain, LangGraph, or Semantic Kernel that connects an LLM to enterprise data sources, complete with evaluation scripts and modular design.
Prepare an architecture walkthrough for cloud-native AI deployment
Completion looks like a system diagram and accompanying notes showing how to containerize an AI service using Docker and deploy it on Azure container services with integrated CI/CD and monitoring.
Assemble evidence of stakeholder collaboration and requirement gathering
Completion looks like structured case notes or a portfolio artifact demonstrating how you translated vague business objectives into technical use cases, prioritized features, and managed cross-functional delivery.
Review enterprise governance, privacy, and cost optimization practices
Completion looks a summary document outlining techniques for managing LLM inference latency, token costs, data privacy, and security guardrails in production environments.
Formulate targeted questions for the AI Center of Excellence team
Completion looks like a list of thoughtful questions regarding the AI CoE's current technology stack on Azure and Snowflake, governance standards, and collaboration models with enterprise architects.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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