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CBRE Hiring Software Engineer – AI & Agentic AI | 2–3 Years Experience | Gurgaon, Hyderabad & Noida


 

CBRE Hiring Software Engineer – AI & Agentic AI | 2–3 Years Experience | Gurgaon, Hyderabad & Noida

CBRE is hiring Software Engineers with 2–3 years of experience in software engineering, data engineering, applied machine learning, or similar technical roles. This is a full-time opportunity focused heavily on AI-driven software development, Generative AI, LLMs, RAG, AI agents, multi-agent systems, cloud platforms, APIs, data pipelines, and enterprise AI implementations.

The role is particularly relevant for engineers who want to move beyond conventional software development into Agentic AI and enterprise AI engineering.

Company: CBRE
Job Title: Software Engineer
Job ID: 278156
Service Line: Corporate Segment
Job Type: Full-time
Posted: August 3, 2026
Experience: 2–3 years
Locations: Gurgaon, Haryana; Hyderabad, Telangana; Noida, Uttar Pradesh
Primary Areas: AI Engineering, Software Engineering, Agentic AI, Machine Learning, Cloud, Data Engineering

Apply for the Software Engineer position at CBRE

CBRE Software Engineer Job Overview

The CBRE Software Engineer role focuses on designing, developing, deploying, and supporting AI-powered enterprise software solutions.

Unlike a traditional Software Engineer opening, this position places significant emphasis on AI agents and multi-agent systems, including prompting, memory management, tool usage, behavioral planning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, vector databases, cloud platforms, MLOps, and enterprise API integrations.

The engineer will work directly with enterprise clients and internal stakeholders to understand business problems and translate them into practical AI solutions.

The role also involves rapidly prototyping ideas, productionizing AI systems, developing secure data pipelines and connectors, troubleshooting AI architectures, training client teams, and leading technical delivery for pilot projects and full-scale implementations.

Key Job Highlights

Job DetailInformation
CompanyCBRE
Job TitleSoftware Engineer
Job ID278156
Job TypeFull-time
Service LineCorporate Segment
PostedAugust 3, 2026
Experience2–3 years
Location 1Gurgaon, Haryana
Location 2Hyderabad, Telangana
Location 3Noida, Uttar Pradesh
DegreeBachelor's in Computer Science, Engineering, or related technical field
ProgrammingPython / Java
AILLMs, RAG, Embeddings, Vector Databases
Advanced AIAgentic AI, Multi-Agent Systems
CloudAWS / GCP / Azure
DevOpsDocker, Kubernetes, MLOps
IntegrationAPIs, Enterprise Systems
Preferred IndustryCommercial Real Estate or adjacent industries

What Does a CBRE Software Engineer Do?

The role combines software engineering + AI/ML + enterprise consulting + implementation.

The selected candidate will work with clients and internal teams to identify business problems that can be solved or improved through AI.

The engineer will then design, prototype, build, deploy, integrate, and support the resulting AI solution.

1. Work Directly With Enterprise Clients

The engineer will collaborate directly with enterprise clients and internal stakeholders to understand:

  • Business objectives.

  • Operational workflows.

  • Data environments.

  • Existing technology.

  • Technical limitations.

  • Opportunities for AI automation.

This means strong communication skills are important alongside technical expertise.

2. Build AI-Driven Solutions

A major responsibility is designing and deploying AI-driven solutions that integrate AI platforms into existing customer systems and workflows.

Candidates should understand how AI can be used to:

  • Automate repetitive processes.

  • Improve decision-making.

  • Augment human workflows.

  • Replace appropriate manual processes.

  • Connect enterprise data with AI systems.

  • Build intelligent applications.

3. Develop AI Agents

One of the most important requirements is hands-on Agentic AI experience.

The engineer will work on AI agents capable of autonomous, proactive, and adaptive behavior.

Relevant concepts include:

  • AI agents.

  • Multi-agent systems.

  • LLM-based agents.

  • Prompting.

  • Memory management.

  • Tool usage.

  • Behavioral planning.

  • Agent orchestration.

  • Autonomous task execution.

This makes Agentic AI one of the strongest keywords for this vacancy.

4. Build Multi-Agent Systems

The role specifically mentions multi-agent systems.

Candidates should understand how multiple AI agents can collaborate to perform complex tasks.

For example, an enterprise AI workflow could potentially use separate agents for:

Data Retrieval → Analysis → Decision Support → Action → Validation

The exact architecture will depend on the business requirement.

5. Work With LLMs

The position requires strong AI literacy, including understanding of Large Language Models (LLMs).

Candidates should understand:

  • LLM fundamentals.

  • Prompt engineering.

  • Context management.

  • Tool calling.

  • Agent behavior.

  • LLM application architecture.

  • Model limitations.

  • Evaluation.

  • Reliability.

6. RAG and Vector Databases

The job specifically mentions:

RAG – Retrieval-Augmented Generation

Embeddings

Vector Databases

Candidates should understand how enterprise knowledge can be retrieved and supplied to an LLM to generate more contextually relevant responses.

A typical architecture may involve:

Enterprise Data → Chunking → Embeddings → Vector Database → Retrieval → LLM → Response

7. Build Data Pipelines and Connectors

The engineer will build connectors and data pipelines to facilitate secure and efficient data movement between:

  • Client systems.

  • Enterprise applications.

  • Data sources.

  • AI platforms.

This means data engineering and integration skills can be valuable for this position.

8. Enterprise API Integration

Candidates should have experience designing and integrating APIs for AI services and enterprise systems.

The job specifically provides examples such as:

  • Salesforce.

  • ServiceNow.

Relevant skills include:

  • API design.

  • REST APIs.

  • API integration.

  • Authentication.

  • Data transformation.

  • Enterprise system integration.

9. Cloud Platforms and MLOps

Hands-on experience with at least one major cloud platform is required.

Relevant platforms include:

  • AWS.

  • Google Cloud Platform (GCP).

  • Microsoft Azure.

The role also asks for familiarity with MLOps tooling, making cloud-based AI deployment experience particularly valuable.

10. Docker and Kubernetes

Candidates should be familiar with containerization technologies such as:

  • Docker.

  • Kubernetes.

These skills are useful for packaging, deploying, scaling, and managing AI-powered applications.

11. Rapid Prototyping

The engineer will work closely with client teams to:

  • Whiteboard ideas.

  • Develop prototypes.

  • Test concepts.

  • Gather feedback.

  • Iterate rapidly.

  • Productionize successful solutions.

This means candidates should be comfortable moving quickly from business problem → technical concept → working prototype → production implementation.

12. AI Architecture Troubleshooting

The Software Engineer will act as a technical solutions expert and provide:

  • Troubleshooting.

  • Technical analysis.

  • Architecture support.

  • Production support.

  • AI-system debugging.

Candidates should therefore understand not only how to build AI applications but also how to diagnose when they fail.

13. Client Training and Documentation

The role includes enabling client teams to use AI solutions effectively.

Responsibilities include:

  • Technical training.

  • Documentation.

  • Knowledge transfer.

  • User enablement.

  • Supporting adoption.

  • Helping teams become self-sufficient.

14. Lead Technical Delivery

The engineer will lead the technical delivery of:

  • Pilot projects.

  • AI prototypes.

  • Enterprise implementations.

  • Full-scale deployments.

The role requires delivering projects on time while meeting defined performance expectations.

Required Qualifications

The supplied job description lists the following requirements:

Education

Bachelor's degree in Computer Science, Engineering, or a related technical field.

Experience

2–3 years of progressive experience in one or more of:

  • Software Engineering.

  • Data Engineering.

  • Applied Machine Learning.

  • Similar technical roles.

Programming

Expert-level proficiency in at least one modern programming language, such as:

  • Python.

  • Java.

AI/ML Knowledge

Demonstrable understanding of:

  • LLMs.

  • RAG.

  • Embeddings.

  • Vector databases.

  • AI/ML fundamentals.

Cloud

Hands-on experience with:

  • AWS.

  • GCP.

  • Azure.

Familiarity with MLOps tooling is also required.

APIs and Integration

Experience with:

  • API design.

  • API integration.

  • AI services.

  • Enterprise systems.

The job description specifically mentions platforms such as Salesforce and ServiceNow.

Agentic AI

Hands-on experience designing, building, and deploying:

  • AI agents.

  • Multi-agent systems.

  • Autonomous AI workflows.

  • Proactive AI systems.

  • Adaptive AI systems.

Containers

Familiarity with:

  • Docker.

  • Kubernetes.

Data

Experience with:

  • Data analytics.

  • Data visualization.

Industry

Prior experience in commercial real estate or adjacent industries is listed as a requirement/preference in the supplied job description.

Is This a Fresher Job?

No.

This is an experienced-hire position.

The job specifically asks for 2–3 years of progressive experience in software engineering, data engineering, applied machine learning, or a similar technical role.

A fresher without relevant professional experience should not treat this as a fresher opening.

Who Should Apply?

This position is especially relevant for professionals such as:

  • AI Engineers.

  • Generative AI Engineers.

  • Machine Learning Engineers.

  • Software Engineers with AI experience.

  • Python Developers with GenAI experience.

  • Java Developers with AI/ML experience.

  • Agentic AI Engineers.

  • LLM Application Developers.

  • Applied ML Engineers.

  • Data Engineers with AI experience.

  • AI Solutions Engineers.

  • Cloud AI Engineers.

Candidates with enterprise consulting or implementation experience may have an additional advantage because the role involves direct client collaboration.

Most Important Skills for ATS

The following keywords should be prioritized when they genuinely match your experience.

Highest-Priority Keywords

Agentic AI
AI Agents
Multi-Agent Systems
LLMs
RAG
Retrieval-Augmented Generation
Embeddings
Vector Databases
Python
Java
AI/ML
Cloud
MLOps

Cloud & Infrastructure

  • AWS

  • GCP

  • Azure

  • Docker

  • Kubernetes

  • Cloud Computing

  • Cloud-Native Development

  • MLOps

AI Engineering

  • Generative AI

  • Large Language Models

  • LLM Applications

  • Prompt Engineering

  • AI Agents

  • Agentic AI

  • Multi-Agent Systems

  • RAG

  • Embeddings

  • Vector Search

  • Vector Databases

  • AI Orchestration

  • Tool Usage

  • Memory Management

  • Behavioral Planning

Software Engineering

  • Python

  • Java

  • API Development

  • API Integration

  • REST APIs

  • SDLC

  • Agile

  • Software Architecture

  • Application Development

  • Production Deployment

  • Troubleshooting

Data Engineering

  • Data Pipelines

  • Data Engineering

  • Data Analytics

  • Data Visualization

  • Data Integration

  • Data Processing

Enterprise Integration

  • Salesforce

  • ServiceNow

  • Enterprise Systems

  • AI Platform Integration

  • Connectors

  • APIs

ATS Resume Strategy

For this job, simply calling yourself a "Software Engineer" is not enough.

Your resume should make the AI engineering component immediately visible.

Weak

Software Engineer with experience in Python and cloud technologies.

Better

Software Engineer with 3 years of experience building Python-based AI applications, LLM workflows, RAG pipelines, API integrations, and cloud-native solutions.

Stronger — If Accurate

AI-focused Software Engineer with 3 years of experience designing and deploying LLM-powered applications, RAG pipelines, AI agents, and multi-agent workflows using Python, cloud platforms, APIs, vector databases, Docker, and Kubernetes.

Do not claim Agentic AI, RAG, Kubernetes, or other technologies unless you genuinely have experience with them.

Suggested Technical Skills Section

Programming: Python, Java
AI/ML: Generative AI, LLMs, RAG, Embeddings, Vector Databases, Prompt Engineering
Agentic AI: AI Agents, Multi-Agent Systems, Agent Orchestration, Tool Usage, Memory Management
Cloud: AWS, GCP, Azure, MLOps
DevOps: Docker, Kubernetes, CI/CD
Integration: REST APIs, API Design, API Integration, Salesforce, ServiceNow
Data: Data Engineering, Data Pipelines, Data Analytics, Data Visualization
Engineering: SDLC, Agile, Software Architecture, Troubleshooting

Only retain skills that accurately reflect the candidate's background.

Freshers vs Experienced

SkillFresher2–3 Years Candidate
Python / JavaAcademic / project experienceProfessional experience
LLMsLearning / projectsApplied production experience
RAGPersonal projectsProduction or client implementations
Vector DatabasesProject exposureHands-on implementation
AI AgentsLearning / experimentationHands-on development
Multi-Agent SystemsAcademic/personal projectsPractical implementation
CloudCertification/projectHands-on cloud experience
MLOpsLearningPractical exposure
APIsProject developmentEnterprise integration
DockerBasic/projectDevelopment and deployment
KubernetesBasic/projectPractical deployment experience
Data PipelinesAcademic/projectProduction data workflows
Client InteractionNot expectedHighly desirable
ConsultingNot expectedAdvantage
Commercial Real EstateNot expectedIndustry advantage

Interview Preparation

This role is likely to require much more than conventional programming questions.

Candidates should prepare across software engineering, AI/ML, Agentic AI, cloud, data, APIs, architecture, and client-facing problem solving.

Python / Java

Prepare for:

  • Object-oriented programming.

  • Data structures and algorithms.

  • Exception handling.

  • API development.

  • Asynchronous programming.

  • Performance optimization.

  • Testing.

  • Software design.

LLM Questions

Be ready to explain:

  • What is an LLM?

  • How does an LLM application differ from traditional software?

  • What causes hallucinations?

  • How do you evaluate LLM responses?

  • How do you manage context?

  • How do you control model output?

  • What are the limitations of prompt-only solutions?

RAG Questions

Expect questions such as:

  • What is RAG?

  • Why use RAG instead of fine-tuning?

  • What are embeddings?

  • What is a vector database?

  • How does semantic search work?

  • How do you improve retrieval quality?

  • What causes poor RAG responses?

  • How would you evaluate a RAG system?

Agentic AI Questions

This is one of the most important interview areas.

Prepare to explain:

  • What is an AI agent?

  • How is an AI agent different from a chatbot?

  • What is an agent tool?

  • What is agent memory?

  • How does an agent decide which tool to use?

  • What is multi-agent architecture?

  • When should multiple agents be used?

  • How do you prevent an agent from taking an incorrect action?

  • How do you evaluate agent performance?

  • How do you handle failures and loops in autonomous workflows?

Cloud and MLOps

Prepare for:

  • AWS/GCP/Azure architecture.

  • Model deployment.

  • Monitoring.

  • Scaling.

  • CI/CD.

  • Model versioning.

  • Data pipelines.

  • Containerization.

  • Docker.

  • Kubernetes.

  • Production AI systems.

API Integration

Prepare for:

  • REST APIs.

  • Authentication.

  • API security.

  • Enterprise integrations.

  • Error handling.

  • Rate limiting.

  • Data transformation.

  • Salesforce integration.

  • ServiceNow integration.

Example Agentic AI Architecture

A candidate should be able to explain an architecture conceptually:

User Request
↓
Orchestrator / Agent
↓
Intent & Planning
↓
Tool Selection
↓
Enterprise APIs / Data Sources
↓
RAG / Vector Database
↓
LLM Reasoning
↓
Validation / Guardrails
↓
Action or Response
↓
Monitoring & Feedback

The exact implementation depends on the business problem, but understanding this flow can help during system-design interviews.

Why This CBRE Role Is Interesting

This is not simply a standard Software Engineer vacancy.

The job combines several fast-growing areas:

Software Engineering + Generative AI + Agentic AI + Cloud + Data Engineering + Enterprise Integration + Consulting

That combination makes the position particularly relevant for engineers looking to build production-grade AI systems rather than only experiment with AI models.

Final Takeaway

The CBRE Software Engineer – Job ID 278156 is an experienced AI-focused software engineering opportunity, not a conventional entry-level Software Engineer role.

The biggest differentiators are Agentic AI, multi-agent systems, LLMs, RAG, embeddings, vector databases, cloud platforms, MLOps, APIs, Docker, Kubernetes, and enterprise AI implementation.

APPLY HERE: Software Engineer




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