Senior Technical Architect

The Senior Technical Architect is a strategic and hands-on technical leader responsible for defining, evolving, and governing architecture across multiple product domains. While Java remains the primary foundation, this role requires the ability to architect enterprise-grade solutions across modern backend and frontend ecosystems (Java, Python, Node.js, React, Angular, etc. ), selecting technologies based on business goals, scalability needs, and long-term sustainability. In addition, this role requires strong awareness and practical exposure to AI-driven systems, including Generative AI, Agentic AI architectures, Retrieval-Augmented Generation (RAG), and AI-assisted development practices.

Responsibilities:

• Own architectural strategy and execution across product lines.
• Design scalable, secure, and AI-enabled systems.
• Drive both functional and non-functional requirements.
• Architect intelligent systems leveraging AI capabilities.
• Use AI-assisted development tools to increase engineering productivity.
• Collaborate with architects and senior engineers to enhance enterprise architecture maturity.
• This is a hands-on leadership role combining deep engineering expertise, AI-forward thinking, and strategic architectural ownership.

Architecture Strategy and Enterprise Vision:

• Define and own the architectural roadmap across multiple product areas.
• Establish architecture principles, standards, and governance practices.
• Lead design reviews and architectural decision-making forums.
• Evaluate and recommend tools, frameworks, platforms, and technologies aligned with evolving business needs.
• Drive modernization initiatives including microservices, API-first design, event-driven systems, and cloud-native adoption.

AI and Intelligent Systems Architecture:

• Design and architect AI-enabled solutions, including: Generative AI integrations, Retrieval-Augmented Generation (RAG) pipelines, Agentic AI workflows and orchestration models, and AI-based automation and decision-support systems.
• Evaluate use cases for AI adoption across product lines.
• Architect scalable AI solution patterns (LLM integration, embeddings, vector databases, prompt orchestration).
• Ensure responsible AI design, including governance, security, and compliance considerations.
• Stay current with advancements in AI, LLM ecosystems, agent frameworks, and emerging AI infrastructure patterns.

AI-Assisted Engineering and Productivity:

• Demonstrate hands-on experience with AI-assisted development tools (e. g., code copilots, AI testing assistants, and AI documentation tools).
• Drive adoption of AI-enabled development workflows to improve the following: Developer productivity, Code quality, Testing efficiency, Documentation and knowledge sharing.
• Establish best practices for secure and responsible usage of AI tools within engineering teams.

End-to-End Solution Ownership:

• Own all functional and non-functional requirements, including: Scalability, Availability, Performance, Security and compliance, Observability and monitoring, and Resilience and disaster recovery.
• Architect robust integration solutions across internal and third-party systems.
• Design high-performance APIs, distributed systems, and data platforms.
• Ensure extensibility, maintainability, and future-proof design.

Technical Leadership and Engineering Excellence:

• Provide deep architectural guidance across backend and frontend domains.
• Mentor architects and senior developers; raise architectural maturity across teams.
• Lead complex distributed systems design and problem-solving.
• Establish coding standards, CI/CD best practices, DevOps maturity, and cloud-native engineering practices.
• Drive technical debt reduction and platform reliability initiatives.
• Serve as an escalation point for critical architectural decisions and production challenges.

Requirements:

• 13+ years of total industry experience.
• Minimum 5+ years of experience as a Technical Architect.
• Proven experience designing large-scale distributed systems.
• Experience architecting integration-heavy enterprise platforms.
• Strong understanding of AI solution architecture patterns.
• Hands-on familiarity with AI-enabled engineering workflows.
• Experience in product-based organizations.
• Experience working in global/distributed engineering teams.
• Bachelor's or Master's degree in Engineering or a related field.

Preferred Qualifications:

• AWS Solutions Architect Certification (or equivalent cloud certification).
• Experience building or integrating AI-powered SaaS platforms.
• Experience with vector databases, embeddings, and LLM orchestration.
• Experience with domain-driven design (DDD).
• Exposure to platform engineering initiatives.

Leadership Competencies:

• Strategic thinker with strong execution capability.
• Influences cross-functional stakeholders effectively.
• Makes balanced, data-driven architectural trade-offs.
• Ownership mindset with accountability.
• Comfortable navigating ambiguity and innovation.
• Excellent communication and presentation skills.

Technology Breadth and Modern Engineering:

• Primary Backend Expertise: Java, Spring Boot, J2EE ecosystem, Architectural Capability Across Python (FastAPI/Django/Flask), Node.js, Microservices and RESTful APIs, Event-driven systems (Kafka/JMS), API gateways, and integration frameworks.
• Frontend Awareness (Architectural Level): React, Angular.
• Cloud and DevOps: AWS (preferred), Azure, or GCP; Serverless architectures; Containers (Docker); Kubernetes; CI/CD pipelines; Infrastructure-as-Code, Observability and monitoring tools.
• Databases: RDBMS (PostgreSQL, MySQL), NoSQL (MongoDB, Cassandra, DynamoDB), and Vector databases (preferred exposure).