New opportunity

Solution Architect- Data Science & AI

ZF Group · Hyderabad, India

About this role

ZF is looking for a Solution Architect - Data Science & AI to join their team in Hyderabad, India, to help shape the future of mobility through scalable Data and AI-driven digital products on cloud platforms. In this hands-on technical leadership role, you will design end-to-end cloud-native architectures, define high-performance system designs, and drive the implementation of SaaS products from concept to production while building and mentoring high-performing teams.

Responsibilities

Design end-to-end architecture for cloud-native Digital products powered by AI/ML.
Define scalable, secure, and high-performance system architectures.
Create architecture blueprints, design patterns, and technical specifications.
Ensure solutions are aligned with best practices for multi-tenancy, scalability and robustness.
Drive the technical design and implementation of SaaS products from concept to production.
Provide hands-on guidance in critical components and complex problem-solving.
Ensure robust integration across services, APIs, and data systems.
Architect and implement solutions on Azure/AWS with strong cloud-native principles.
Design and optimize microservices, containerized deployments, and serverless architectures.
Establish CI/CD pipelines and DevOps practices for continuous delivery.
Design and integrate scalable data platforms including data lakes, warehouses, and lakehouses.
Enable efficient data pipelines and feature engineering workflows.
Support deployment and integration of AI/ML models into production using MLOps practices.
Build, mentor, and actively participate in the reskilling of the team.

Required qualifications

10+ Years of experience in software engineering Development and solution architecture.
Proven track record of designing and delivering SaaS products in production environments.
Strong expertise in cloud platforms such as Azure or AWS and cloud-native architectures.
Solid experience with microservices, REST APIs, and distributed systems design.
Hands-on experience with data platforms like Azure, Databricks, BigQuery, or Redshift.
Experience deploying and integrating AI/ML solutions into applications.
Proficiency in Python, Java, or similar programming languages.
Strong understanding of DevOps, CI/CD, Docker, and Kubernetes.

Preferred qualifications

Experience in Applied AI, Generative AI, or LLM-based applications.
Familiarity with MLOps frameworks and tools including MLflow, Kubeflow, and Airflow.
Experience with real-time or streaming data systems such as Kafka, Spark Streaming, and Flink.
Exposure to multi-tenant SaaS architecture and subscription-based models.
Understanding of data governance, compliance, and security frameworks.

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

Skills for this role

PythonJavaAzureAWSDockerKubernetesCI/CDMLflowKubeflowAirflowKafkaSpark StreamingFlinkDatabricksBigQueryRedshift

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 Cloud-Native Architecture & SaaS Delivery
Design and drive the technical implementation of scalable, secure, and production-ready SaaS products from concept to production on Azure or AWS, ensuring adherence to multi-tenancy and robust microservices principles.
AI/ML Integration and Data Platform Scalability
Integrate AI/ML models into enterprise applications using MLOps practices, connecting scalable data platforms like data lakes, warehouses, and lakehouses to handle structured and unstructured data efficiently.
Technical Leadership, Mentorship, and Team Reskilling
Build, guide, and mentor engineering and data science teams through changing technical landscapes, providing hands-on code and architecture problem-solving guidance alongside management support.

Likely challenges to think through

Balancing Hands-on Technical Execution with Team Mentorship
The role demands deep hands-on participation in critical architecture components and complex problem-solving while simultaneously building, mentoring, and upskilling the team. To prepare, practice articulating how you delegate operational tasks while maintaining code and design quality standards through code reviews and blueprinting.
Scaling AI/ML Models into Production with MLOps
Bridging experimental AI/ML models with production-grade enterprise software introduces infrastructure and reliability challenges. Prepare by reviewing MLOps patterns, CI/CD pipelines for containerized deployments, and feature engineering workflows across cloud data platforms.

Practice your answers

Technical practice

Designing Multi-Tenant SaaS Cloud Architectures

What to coverDiscuss how you approach multi-tenancy isolation, security, and resource allocation when designing cloud-native SaaS solutions on Azure or AWS. Explain trade-offs between shared database versus isolated database models and how containerization via Kubernetes supports workload scaling.

Integrating AI and ML Models into Production Systems

What to coverExplain your approach to deploying trained machine learning models into live enterprise applications. Address latency requirements, API design, model versioning, and how MLOps tools like MLflow or Kubeflow manage the pipeline lifecycle.

Distributed Data Platforms and Lakehouses

What to coverCompare data lakes, data warehouses, and modern lakehouse architectures (such as Databricks or BigQuery) for handling structured and unstructured data at scale. Discuss ingestion patterns and feature engineering workflows.

Real-Time Data Streaming and Pipeline Processing

What to coverDescribe how you incorporate streaming data systems like Kafka, Spark Streaming, or Flink into architectures requiring real-time analytics. Address fault tolerance, exactly-once processing semantics, and backpressure management.

Microservices and REST API Design Principles

What to coverOutline best practices for designing scalable microservices and robust REST APIs in Python or Java. Focus on service discovery, API versioning, asynchronous communication patterns, and maintaining low coupling.

CI/CD Pipelines and DevOps Best Practices

What to coverExplain how you establish continuous integration and continuous delivery (CI/CD) pipelines for containerized applications using Docker and Kubernetes. Detail automated testing, artifact management, and rollout strategies.

Generative AI and LLM Application Integration

What to coverDiscuss architectural considerations when integrating Large Language Models or Generative AI components into existing software products. Address context window limits, retrieval-augmented generation (RAG) patterns, and token usage optimization.

Data Governance, Compliance, and Security Frameworks

What to coverExplain how you enforce data governance, privacy compliance, and security standards across cloud-based data platforms. Discuss access control policies, encryption at rest/in transit, and audit logging.

Behavioural practice

Mentoring Engineers and Driving Team Reskilling

What to coverDescribe a situation where you had to mentor engineers or lead a team through a significant technical transition or reskilling initiative. Use the Situation-Action-Result format to highlight how you identified skill gaps, provided guidance, and measured progress.

Navigating Technical Disagreements on Architecture

What to coverShare an experience where you faced a conflict with product or engineering stakeholders regarding a system architecture or trade-off. Explain how you evaluated options objectively, communicated risks, and reached a consensus.

Delivering Complex SaaS Products from Concept to Production

What to coverProvide an example of a time you owned the end-to-end technical design and delivery of a complex SaaS product. Detail how you managed scope, handled critical bottlenecks, and ensured high performance in production.

Balancing Hands-On Technical Execution with Leadership

What to coverDiscuss how you balance writing code and diving into complex architectural problem-solving with broader leadership responsibilities like team planning and code reviews. Give an example of how you prioritized your time.

Sharpen your knowledge

Topics to prepare

Cloud-Native Multi-Tenant SaaS Architecture
Review core architectural patterns for scalable, secure, and multi-tenant SaaS platforms on Azure or AWS. Exercise: Draw a reference architecture blueprint for a containerized multi-tenant service handling data isolation and high availability. Relevance: Directly aligns with the core responsibility of designing end-to-end architecture and SaaS products from concept to production.
MLOps and AI Model Production Integration
Study lifecycle management, deployment, and integration patterns for AI/ML models in production environments. Exercise: Map out an MLOps workflow tracking model versioning and continuous integration using tools like MLflow or Kubeflow. Relevance: Addresses the requirement to support deployment and integration of AI/ML solutions into applications using MLOps practices.
Distributed Data Platforms and Lakehouse Design
Examine the design of modern data platforms incorporating data lakes, data warehouses, and lakehouses. Exercise: Design a data ingestion and feature engineering pipeline handling both structured and unstructured data at scale. Relevance: Matches requirements for hands-on experience with data platforms like Databricks, BigQuery, or Redshift.

Skills to revise

Kubernetes and Container Orchestration
Practice containerizing applications with Docker and deploying/managing them on Kubernetes clusters. Exercise: Write deployment and service manifests for a microservice architecture with auto-scaling rules. Relevance: Fulfills the requirement for strong understanding of containerized deployments, microservices, and distributed systems design.

Your preparation checklist

Draft Cloud-Native SaaS Reference Architecture
Create an architecture blueprint demonstrating a multi-tenant, containerized SaaS product deployed on Azure or AWS, highlighting microservices, REST APIs, and serverless components.
Map MLOps and Model Integration Pipelines
Prepare a documented workflow illustrating how AI/ML models are deployed into production, integrating feature engineering workflows and MLOps tools such as MLflow or Kubeflow.
Compile Evidence of Distributed Data Platform Experience
Organize past project examples involving data lakes, data warehouses, and lakehouses (such as Databricks or BigQuery) processing structured and unstructured data at scale.
Prepare Technical Leadership and Mentorship Examples
Document specific examples of building high-performing engineering teams, conducting performance reviews, and leading technical reskilling initiatives in changing environments.
Review Real-Time Data Streaming and Governance Frameworks
Refresh knowledge on streaming systems like Kafka or Spark Streaming and data governance, security, and compliance frameworks relevant to enterprise platforms.
Formulate Targeted Questions for the Employer
Prepare thoughtful questions regarding the primary AI-driven digital products in mobility, team structure, and expected collaboration between engineering and data science teams.

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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