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
- 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:
- Application Development (monoliths, microservices, APIs)
- Data Platforms (Data Lakes, Lakehouse, Data Warehouses)
- Cloud-native systems (AWS / Azure / GCP)
- AI/ML & Generative AI solutions
- Enable seamless integration across systems and domains.
- 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.
- 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:
- Model lifecycle (training, deployment, monitoring)
- 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).
AWS,pyspark,sql,java,python,glue,lambda,athena
- Modernization & Transformation
- Lead application and data modernization initiatives:
- Legacy → Cloud-native
- Monolith → Microservices
- Traditional DW → Modern Data Platforms
- Define and execute migration strategies (rehost, replatform, refactor, rebuild).
- 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.
- Security, Governance & Compliance
- Implement end-to-end security architecture.
- Define identity, access control, and data protection mechanisms.
- Ensure compliance with enterprise and regulatory standards.
- 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.
Required Skills & Experience
Core Technical Expertise- Strong experience across:
- Application Development
- Data Engineering & Platforms
- Cloud Architecture
- Deep understanding of distributed systems and system design.
Application Development- Proficiency in Java, Python, or similar languages.
- Experience with:
- Microservices architecture
- API design and integration
- Event-driven systems
Data & Analytics- Experience with:
- Data Lakes, Data Warehouses, Lakehouse architectures
- ETL/ELT pipelines and data modeling
- Familiarity with big data processing frameworks.
Cloud (Mandatory – Any One)- AWS / Azure / GCP with experience in:
- Application services
- Data platforms
- AI/ML services
AI/ML & GenAI (Preferred)- Exposure to:
- Machine Learning lifecycle and MLOps
- Generative AI / LLM-based solutions
- Understanding of AI integration patterns.
DevOps & Platform Engineering- Experience with:
- CI/CD pipelines
- Infrastructure as Code (Terraform or similar)
- 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.
