Role & responsibilities :

We are looking for a highly versatile and experienced Technical Architect who can design, build, and scale end-to-end technology solutions across multiple domains including Data Platforms, Application Development, Cloud, Machine Learning, and Generative AI.

The ideal candidate is a multi-domain architect with strong fundamentals and the ability to architect across technologies, drive modernization initiatives, and guide teams in delivering scalable, secure, and high-performance systems.

Key Responsibilities :

1. Enterprise Architecture & Solution Design :

- Design end-to-end architectures spanning applications, data platforms, AI/ML systems, and cloud ecosystems.

- Define architecture principles, standards, and best practices.

- Create HLD/LLD design artifacts.

- Ensure solutions are scalable, resilient, secure, and cost-efficient.

2. Multi-Domain Technology Architecture :

- Architect solutions across :

1. Application Development (monoliths, microservices, APIs)

2. Data Platforms (Data Lakes, Lakehouse, Data Warehouses)

3. Cloud-native systems (AWS / Azure / GCP)

4. AI/ML & Generative AI solutions

- Enable seamless integration across systems and domains.

3. Application Architecture & Development :

- Design modern application architectures (microservices, event-driven, API-first).

- Define integration patterns (REST, GraphQL, messaging, streaming).

- Ensure best practices in performance, scalability, and reliability.

4. Data & Analytics Architecture :

- Architect data ecosystems including ingestion, processing, storage, and consumption.

- Support batch, streaming, and real-time processing.

- Define data modeling, governance, lineage, and quality frameworks.

5. AI/ML & Generative AI Enablement :

- Design and integrate ML and GenAI solutions into enterprise platforms.

- Define architectures for :

1. Model lifecycle (training, deployment, monitoring)

2. LLM integration and AI pipelines

- Ensure responsible, scalable AI implementations.

6. Cloud & Platform Engineering :

- Architect and implement solutions on AWS / Azure / GCP.

- Leverage cloud-native services for applications, data, and AI workloads.

- Drive platform engineering, scalability, and cost optimization (FinOps).

7. Modernization & Transformation :

- Lead application and data modernization initiatives :

1. Legacy - Cloud-native

2. Monolith - Microservices

3. Traditional DW - Modern Data Platforms

- Define and execute migration strategies (rehost, replatform, refactor, rebuild).

8. DevOps, Automation & Observability :

- Implement CI/CD pipelines across application, data, and ML workflows.

- Promote Infrastructure as Code (IaC) and automation.

- Define monitoring, logging, and observability frameworks.

9. Security, Governance & Compliance :

- Implement end-to-end security architecture.

- Define identity, access control, and data protection mechanisms.

- Ensure compliance with enterprise and regulatory standards.

10. Leadership & Collaboration :

- Provide technical leadership and mentorship.

- Collaborate with stakeholders, product teams, and engineering teams.

- Contribute to solutioning, pre-sales, and innovation initiatives.

- Drive adoption of modern engineering and architectural best practices.

Preferred candidate profile :

Required Skills & Experience :

Core Technical Expertise :

- Strong experience across :

1. Application Development

2. Data Engineering & Platforms

3. Cloud Architecture

- Deep understanding of distributed systems and system design.

Application Development :

- Proficiency in Java, Python, or similar languages.

- Experience with :

1. Microservices architecture

2. API design and integration

3. Event-driven systems

Data & Analytics :

- Experience with :

1. Data Lakes, Data Warehouses, Lakehouse architectures

2. ETL/ELT pipelines and data modeling

- Familiarity with big data processing frameworks.

Cloud (Mandatory Any One) :

- AWS / Azure / GCP with experience in :

1. Application services

2. Data platforms

3. AI/ML services

AI/ML & GenAI (Preferred) :

- Exposure to :

1. Machine Learning lifecycle and MLOps

2. Generative AI / LLM-based solutions

- Understanding of AI integration patterns.

DevOps & Platform Engineering :

- Experience with :

1. CI/CD pipelines

2. Infrastructure as Code (Terraform or similar)

3. Containerization (Docker, Kubernetes)

Preferred Qualifications :

- Experience in modernization and transformation programs.

- Exposure to multi-cloud or hybrid architectures.

- Certifications in cloud, architecture, or data engineering.

- Experience in cost optimization and FinOps.

Soft Skills :

- Strong architectural thinking and problem-solving.

- Excellent communication and stakeholder management.

- Ability to translate business requirements into scalable solutions.

- Leadership mindset with focus on mentoring and innovation.