New opportunity

Technical Specialist

Carrier · Hyderabad, India

About this role

The Technical Specialist will serve as an Applied AI Specialist within Carrier's Aftermarket AI & Digital Solutions team. This role is responsible for leading the development of AI-enabled digital solutions to enhance aftermarket service business, focusing on predictive maintenance, field service efficiency, customer experience, and warranty analytics. Responsibilities include designing and deploying AI/ML, Generative AI, and intelligent automation solutions, building enterprise applications using LLMs, RAG, and agentic frameworks, and developing AI-powered assistants and copilots. The role involves developing scalable backend services and APIs using Python, managing cloud-based AI platform development on Azure, AWS, or Google Cloud, and collaborating with MLOps teams for model deployment and monitoring. Data engineering tasks include building pipelines for structured and unstructured data to support model development. Candidates must have 15–20 years of experience in software engineering, AI/ML, or data engineering, with strong programming skills in Python and object-oriented design. Required qualifications include hands-on experience with AI frameworks (LangChain, LangGraph, Hugging Face, TensorFlow, PyTorch), knowledge of RAG, prompt engineering, and vector databases, and familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines. A Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field is required. The role requires strong analytical, problem-solving, and communication skills to partner with product owners and architects to deliver scalable AI solutions.

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

Skills for this role

PythonMachine LearningGenerative AILarge Language Models (LLMs)LangChainLangGraphHugging FaceTensorFlowPyTorchRetrieval-Augmented Generation (RAG)Prompt EngineeringVector DatabasesAzureAWSGoogle CloudDockerKubernetesREST APIsGitCI/CDMLOps

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

Development of Scalable AI-Enabled Aftermarket Solutions
The core mandate is to design and deploy AI/ML and Generative AI solutions specifically for aftermarket service business, including predictive maintenance and warranty analytics. You must demonstrate the ability to translate business requirements into scalable, production-ready AI applications that deliver measurable value.
Enterprise AI Application Architecture
You are expected to build sophisticated enterprise applications using LLMs, RAG, and agentic frameworks. This requires deep technical proficiency in integrating these models with backend services and APIs while ensuring the architecture remains maintainable and robust.
Cross-Functional Technical Collaboration
Success in this role requires partnering with product owners, architects, and data engineering teams. You must be prepared to provide technical guidance to junior engineers and ensure that AI solutions are aligned with broader enterprise cloud and MLOps standards.

Likely challenges to think through

Balancing Innovation with Production Stability
The role requires implementing cutting-edge Generative AI and agentic frameworks while ensuring they meet enterprise-grade production standards. Prepare to discuss how you manage the trade-off between rapid prototyping and the rigorous requirements of MLOps, model monitoring, and CI/CD pipelines.
Managing Complex Data Pipelines for AI
You will be responsible for both structured and unstructured data pipelines to support model development. A significant challenge is ensuring high-quality data ingestion and feature engineering for diverse aftermarket use cases; be ready to explain your approach to data quality and pipeline scalability.

Practice your answers

Technical practice

Architecting RAG Systems for Enterprise

What to coverExplain your approach to building a Retrieval-Augmented Generation (RAG) pipeline for enterprise data. Discuss how you handle data ingestion, chunking strategies, vector database selection, and the trade-offs between retrieval accuracy and latency.

Agentic Frameworks and Workflow Automation

What to coverDescribe your experience using agentic frameworks like LangGraph to build autonomous AI assistants. Focus on how you manage state, handle tool-use, and ensure reliability in complex, multi-step workflows.

Scalable Python Backend Design

What to coverGiven the requirement for scalable backend services, discuss how you design microservices in Python to support AI workloads. Address concurrency, API design, and how you ensure code maintainability in large-scale systems.

MLOps and Model Lifecycle Management

What to coverHow do you collaborate with MLOps teams to ensure models are production-ready? Discuss your experience with CI/CD pipelines for AI, model monitoring, and strategies for continuous improvement after deployment.

Data Engineering for AI Pipelines

What to coverExplain your process for preparing structured and unstructured data for model training and inference. Detail how you handle feature engineering and ensure data quality within automated pipelines.

Cloud-Native AI Deployment

What to coverCompare the challenges of deploying AI applications on Azure, AWS, or Google Cloud. Discuss your experience with containerization (Docker/Kubernetes) and how you optimize resource utilization for AI workloads.

Prompt Engineering and Model Optimization

What to coverHow do you approach prompt engineering to improve the accuracy and consistency of LLM outputs? Discuss techniques for evaluating model performance and optimizing for specific business use cases.

Object-Oriented Design in AI Systems

What to coverHow do you apply object-oriented design principles to build reusable AI frameworks and shared components? Provide an example of how modular design has helped you manage complexity in a large-scale project.

Behavioural practice

Leading Technical Strategy with Stakeholders

What to coverDescribe a time you partnered with product owners or architects to define the technical direction for an AI solution. How did you balance business requirements with technical feasibility and scalability?

Mentoring and Knowledge Sharing

What to coverProvide an example of how you have provided technical guidance to junior engineers. How do you foster a culture of knowledge sharing and ensure team growth in emerging AI technologies?

Problem-Solving in Complex Deployments

What to coverTell me about a challenging technical issue you encountered during the deployment of an AI model. What was the situation, what actions did you take to troubleshoot, and what was the final result?

Adapting to Emerging Technologies

What to coverThe AI field evolves rapidly. Describe a situation where you had to quickly learn and integrate a new AI tool or framework into an existing project. How did you assess its value and manage the integration process?

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architecture
Review the end-to-end flow of RAG systems, specifically focusing on document ingestion, vector database indexing, and retrieval strategies. This is critical for the role's focus on building enterprise AI applications and copilots.
Agentic AI Frameworks
Study the conceptual design of agentic workflows using tools like LangGraph. Focus on how agents manage state, tool selection, and multi-step reasoning to automate complex aftermarket service tasks.
MLOps and Model Lifecycle Management
Examine the lifecycle of production AI models, including monitoring for drift, versioning, and CI/CD integration. This is essential for the role's requirement to collaborate with MLOps teams for deployment and maintenance.

Skills to revise

Python for Backend and AI Integration
Practice building scalable REST APIs and microservices in Python. Focus on integrating these services with AI frameworks like LangChain to ensure production-ready code.
Containerization and Orchestration
Develop a sample project that containerizes a Python-based AI application using Docker and deploys it to a Kubernetes cluster. This demonstrates the ability to manage cloud-native AI workloads.
Prompt Engineering and Optimization
Create a set of structured prompts for a specific domain, such as predictive maintenance, and test them against different LLMs to optimize for accuracy and cost. This directly addresses the requirement for developing AI-powered assistants.

Your preparation checklist

Map Experience to Aftermarket AI Use Cases
Prepare to discuss how your 15+ years of experience in software and AI can be applied to Carrier's specific needs: predictive maintenance, field service efficiency, and warranty analytics. Create a summary of past projects where you delivered measurable business value using AI/ML.
Develop a RAG and Agentic Framework Walkthrough
Prepare a technical deep dive into a project where you built an enterprise application using LLMs, RAG, and agentic frameworks. Be ready to explain your choice of vector databases and how you handled data ingestion for both structured and unstructured data.
Demonstrate MLOps and Cloud Proficiency
Prepare to explain your experience with end-to-end AI lifecycles, including model deployment, monitoring, and CI/CD pipelines on Azure, AWS, or Google Cloud. Highlight your familiarity with containerization tools like Docker and Kubernetes in production environments.
Prepare for Technical Collaboration Discussions
Formulate examples of how you have partnered with product owners and architects to translate business requirements into scalable AI solutions. Be ready to discuss your approach to mentoring junior engineers and fostering knowledge-sharing within a team.
Draft Strategic Questions for the Interviewer
Prepare thoughtful questions regarding the current maturity of Carrier's MLOps processes, the primary data challenges in their aftermarket service business, and how the team balances rapid prototyping with production-grade software engineering standards.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…